Data visualizataions of GAM data Generated by Housen bootstrapping 500 times
na.count<-function(x) sum(is.na(x))
na.mean<-function(x) ifelse(is.nan(mean(x,na.rm=T)),NA,mean(x,na.rm=T))#Defining the data I/O directory
## change root.path as needed
root.path<-"C:\\Users\\tfens\\R_REPOS\\Flux_processing\\Concord_R_Code\\Concord_Post_Process\\"
# root.path<-"D:\\Housen\\Flux\\Data-exploring\\02_Concord_Eden\\"
#output folder
out.path<-paste0(root.path, "02_output\\01_GAM_output\\")
#load the RDA data produced in 12-Concord_bootstrap_GAMS_all #Housen uploaded the data as rda files to google drive.
all_data <- readRDS (file = "2022-04-20_all_data.rda" )
all_daily_data <- readRDS (file = "2022-04-20_all_data_daily.rda" )
all_filled_cum_matrix <- readRDS(file = "2022-04-20_all_filled_matrix.rda")
all_filled_matrix <- readRDS(file = "2022-04-20_all_filled_matrix.rda")
all_filled_matrix <- readRDS(file = "2022-04-20_all_predict_matrix.rda")
all_data$all_pre_datawrite.csv(
all_data$all_pre_data,
paste0(out.path, Sys.Date(), "_all_pre_compost_data.csv"),
quote = T,
row.names = F
)#Figure for cumulative filled NEE by pre and post compost and group
col.code2 <- list(col.name = c("control", "treatment"),
col = c("firebrick1", "deepskyblue"))
##### Figure for filled NEE
png(
paste0(
out.path,
Sys.Date(),
"_filled_cum_NEE_bygroups.png"
),
width = 6.5,
height = 3.5,
units = "in",
pointsize = 10,
res = 400
)
par(mar = c(4, 0.2, 0.5, 0.2), oma = c(0, 4, 0, 0.5), mfrow = c(1, 2))
plot(all_data[[1]]$TIMESTAMP,
all_data[[1]]$NEE_filled_cum_control_mean,
pch = 20,
cex = 0.7,
col = col.code2$col[1],
las = 1,
ylab = "",
xlab = "",
ylim = c(-100, 200))
mtext(side = 2,
expression(Cumulative~CO[2]~flux~'('*g~C~m^{-2}*')'),
outer = T,
line = 2.5)
mtext(side = 1,
"2019-2020",
outer = F,
line = 2.5)
points(all_data[[1]]$TIMESTAMP,
all_data[[1]]$NEE_filled_cum_treatment_mean,
pch = 20,
cex = 0.7,
col = col.code2$col[2])
lines(all_data[[1]]$TIMESTAMP,
all_data[[1]]$NEE_filled_cum_control_q025,
col = col.code2$col[1],
lty = 2)
lines(all_data[[1]]$TIMESTAMP,
all_data[[1]]$NEE_filled_cum_control_q975,
col = col.code2$col[1],
lty = 2)
lines(all_data[[1]]$TIMESTAMP,
all_data[[1]]$NEE_filled_cum_treatment_q025,
col = col.code2$col[2],
lty = 2)
lines(all_data[[1]]$TIMESTAMP,
all_data[[1]]$NEE_filled_cum_treatment_q975,
col = col.code2$col[2],
lty = 2)
legend("topleft",
fill = col.code2$col,
border = NA,
legend = col.code2$col.name,
ncol = 2,
cex = 0.7,
bty = "n")
abline(h = 0, col = "black")
plot(all_data[[2]]$TIMESTAMP,
all_data[[2]]$NEE_filled_cum_control_mean,
pch = 20,
cex = 0.7,
col = col.code2$col[1],
las = 1,
ylab = "",
xlab = "",
yaxt = "n",
ylim = c(-100, 200))mtext(side = 1,
"2020-2021",
outer = F,
line = 2.5)
points(all_data[[2]]$TIMESTAMP,
all_data[[2]]$NEE_filled_cum_treatment_mean,
pch = 20,
cex = 0.7,
col = col.code2$col[2])
lines(all_data[[2]]$TIMESTAMP,
all_data[[2]]$NEE_filled_cum_control_q025,
col = col.code2$col[1],
lty = 2)
lines(all_data[[2]]$TIMESTAMP,
all_data[[2]]$NEE_filled_cum_control_q975,
col = col.code2$col[1],
lty = 2)
lines(all_data[[2]]$TIMESTAMP,
all_data[[2]]$NEE_filled_cum_treatment_q025,
col = col.code2$col[2],
lty = 2)
lines(all_data[[2]]$TIMESTAMP,
all_data[[2]]$NEE_filled_cum_treatment_q975,
col = col.code2$col[2],
lty = 2)
abline(h = 0, col = "black")
dev.off()null device
1 #figure for gap filled daily NEE. Updated to include the error range for the post compost control side daily data
col.code4 <- list(col.name = c("control", "treatment"),
col = c("firebrick4", "deepskyblue4"),
col2 = c(rgb(1, 0, 0, 0.3), rgb(0, 0, 1, 0.3)),
col3 = c("lightcoral", "deepskyblue4"))
##### Figure for gap-filled daily NEE
png(
paste0(
out.path,
Sys.Date(),
"_filled_daily_NEE_bygroups.png"
),
width = 6.5,
height = 3.5,
units = "in",
pointsize = 10,
res = 400
)
par(mar = c(4, 0.2, 0.5, 0.2), oma = c(0, 4, 0, 0.5), mfrow = c(1, 2))
plot(0,
0,
las = 1,
ylab = "",
xlab = "",
ylim = c(-10, 10),
xlim = range(all_daily_data[[1]]$Doy_water),
type = "n")
mtext(side = 1,
"2019-2020",
outer = F,
line = 2.5)
mtext(side = 2,
expression(CO[2]~flux~'('*g~C~m^{-2}~d^{-1}*')'),
outer = T,
line = 2.5)
polygon(c(all_daily_data[[1]]$Doy_water,
rev(all_daily_data[[1]]$Doy_water)),
c(all_daily_data[[1]]$NEE_filled_control_q975,
rev(all_daily_data[[1]]$NEE_filled_control_q025)),
col = col.code4$col3[1],
border = NA)
polygon(c(all_daily_data[[1]]$Doy_water,
rev(all_daily_data[[1]]$Doy_water)),
c(all_daily_data[[1]]$NEE_filled_treatment_q975,
rev(all_daily_data[[1]]$NEE_filled_treatment_q025)),
col = col.code4$col2[2],
border = NA)
points(all_daily_data[[1]]$Doy_water,
all_daily_data[[1]]$NEE_filled_control_mean,
pch = 20,
cex = 0.7,
col = col.code4$col[1])
points(all_daily_data[[1]]$Doy_water,
all_daily_data[[1]]$NEE_filled_treatment_mean,
pch = 20,
cex = 0.7,
col = col.code4$col[2])
legend("topleft",
fill = col.code4$col,
border = NA,
legend = col.code2$col.name,
ncol = 2,
cex = 0.7,
bty = "n")
plot(0,
0,
las = 1,
ylab = "",
xlab = "",
yaxt = "n",
ylim = c(-10, 10),
xlim = range(all_daily_data[[2]]$Doy_water),
type = "n")mtext(side = 1,
"2020-2021",
outer = F,
line = 2.5)
polygon(c(all_daily_data[[2]]$Doy_water,
rev(all_daily_data[[2]]$Doy_water)),
c(all_daily_data[[2]]$NEE_filled_control_q975,
rev(all_daily_data[[2]]$NEE_filled_control_q025)),
col = col.code4$col3[1],
border = NA)
polygon(c(all_daily_data[[2]]$Doy_water,
rev(all_daily_data[[2]]$Doy_water)),
c(all_daily_data[[2]]$NEE_filled_treatment_q975,
rev(all_daily_data[[2]]$NEE_filled_treatment_q025)),
col = col.code4$col2[2],
border = NA)
points(all_daily_data[[2]]$Doy_water,
all_daily_data[[2]]$NEE_filled_control_mean,
pch = 20,
cex = 0.7,
col = col.code4$col[1])
points(all_daily_data[[2]]$Doy_water,
all_daily_data[[2]]$NEE_filled_treatment_mean,
pch = 20,
cex = 0.7,
col = col.code4$col[2])
dev.off()null device
1 ## save output
saveRDS(all_daily_data,
paste0(out.path, Sys.Date(), "_all_daily_data.rda"))#Tommy just getting a sense of the lists and data
#Reco_mean and quantiles
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$RECO_predict_control_mean, ylim = c(0,35) )
par(new=TRUE)
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$RECO_predict_control_q975, col = 'red', ylim = c(0,35))
par(new = TRUE)plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$RECO_predict_control_q025, col = 'blue', ylim = c(0,35))
#NEE
#1/2 hourly
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_control_mean)
#cumulative with error
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_cum_control_q975, col='red', ylim = c(0,200)
)
par(new=TRUE)
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_cum_control_mean, col='grey', ylim = c(0,200))
par(new = TRUE)
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_cum_control_q025 , col='red', ylim = c(0,200)
)
#Creating a new data column for respiration #combination of Day Reco_predict_mean annd NEE_filled__means #Our night time respiration data will be directly measured, while our daytime respiration data will be modeled.
#This is actually unnneccesary. Just going to use NEE- Reco predict and then force all nightime GPP to zero.
####Control side pre-compost#########################
# creating new column called RECO_control_FIN
all_data$all_pre_data$RECO_control_FIN <- (all_data$all_pre_data$RECO_predict_control_mean * 1)
#assigning all night time respiration gap fill night NEE
all_data$all_pre_data$RECO_control_FIN[(all_data$all_pre_data$Rg <= 10 )]<-
all_data$all_pre_data$NEE_filled_control_mean [(all_data$all_pre_data$Rg <= 10 )]
#plot and summary of recon control fin (observed nighttime NEE, and modeled Reco)
plot(all_data$all_pre_data$RECO_control_FIN )
summary(all_data$all_pre_data$RECO_control_FIN) Min. 1st Qu. Median Mean 3rd Qu. Max.
-5.665 2.079 2.879 3.621 4.149 18.302 #plot and summary of just modeled Reco
plot(all_data$all_pre_data$RECO_predict_control_mean)
summary(all_data$all_pre_data$RECO_predict_control_mean) Min. 1st Qu. Median Mean 3rd Qu. Max.
-1.169 2.101 2.879 3.621 4.131 18.302 #Thought I had to do all this below, but really do not need to. Can just use the filled NEE (see above). Filled NEE has actual NEE values for night time plus GAM filled. This ensures both actually measured values, excluding bad data and the best modeled nigh time NEE/ Reco
#Creating observed NEE variable to be used for nighttime reco
# all_data[[1]]$NEE_control_observed[all_data[[1]]$treatment== "control_pre_compost"] <- (all_data[[1]]$NEE[all_data[[1]]$treatment == "control_pre_compost"])
#
# plot(all_data$all_pre_data$NEE_control_observed) # around 1/2 night observed reco data is neg. maybe just use modeled data?
#
# #looking at how the new variable look
# summary(all_data$all_pre_data$NEE_control_observed)
# summary(all_data$all_pre_data$RECO_control_FIN)
#
# #assigning night time measured NEE values to the associated night time respiration values
#
# all_data$all_pre_data$RECO_control_FIN[all_data$all_pre_data$Rg <= 10 & !is.na( all_data$all_pre_data$NEE_control_observed)]<-
# all_data$all_pre_data$NEE_control_observed[all_data$all_pre_data$Rg<=10 & !is.na( all_data$all_pre_data$NEE_control_observed)]
#
#
# #seeing how this shakes out
# plot(all_data$all_pre_data$RECO_control_FIN )
#
# summary(all_data$all_pre_data$RECO_control_FIN)
#
# summary(all_data$all_pre_data$RECO_predict_control_mean)
#
# summary(all_data$all_pre_data$NEE_filled_control_mean)
#
# plot(all_data$all_pre_data$TIMESTAMP, all_data$all_pre_data$RECO_control_FIN, col = 'red', ylim = c(-10, 30))
# par(new= TRUE)
# plot(all_data$all_pre_data$TIMESTAMP, all_data$all_pre_data$RECO_predict_control_mean, col = 'blue', ylim = c(-10, 30))
##############Control side post-compost######################
#creating new column called RECO_control_FIN
all_data$all_post_data$RECO_control_FIN <- (all_data$all_post_data$RECO_predict_control_mean * 1)
#assigning all night time respiration gap fill night NEE
all_data$all_post_data$RECO_control_FIN[(all_data$all_post_data$Rg <= 10 )]<-
all_data$all_post_data$NEE_filled_control_mean [(all_data$all_post_data$Rg <= 10 )]
plot(all_data$all_post_data$RECO_control_FIN )
summary(all_data$all_post_data$RECO_control_FIN) Min. 1st Qu. Median Mean 3rd Qu. Max.
-3.654 1.398 2.064 2.061 2.673 11.756 summary(all_data$all_post_data$NEE_filled_control_mean) Min. 1st Qu. Median Mean 3rd Qu. Max.
-13.7967 -0.8410 1.3248 0.4075 2.3158 11.7562 ###############treatment side pre compost############################
all_data$all_pre_data$RECO_treatment_FIN <- (all_data$all_pre_data$RECO_predict_treatment_mean * 1)
###########treatment side post-compost###############
#creating new column called RECO_treatment_FIN
all_data$all_post_data$RECO_treatment_FIN <- (all_data$all_post_data$RECO_predict_treatment_mean * 1)
#assigning all night time respiration gap fill night NEE pre compost
all_data$all_pre_data$RECO_treatment_FIN[(all_data$all_pre_data$Rg <= 10 )]<-
all_data$all_pre_data$NEE_filled_treatment_mean [(all_data$all_pre_data$Rg <= 10 )]
plot(all_data$all_pre_data$RECO_treatment_FIN )
summary(all_data$all_pre_data$RECO_treatment_FIN) Min. 1st Qu. Median Mean 3rd Qu. Max.
-15.914 1.935 2.978 2.961 3.946 17.985 summary(all_data$all_pre_data$NEE_filled_treatment_mean) Min. 1st Qu. Median Mean 3rd Qu. Max.
-17.91950 -3.50352 1.41118 -0.07219 3.14213 18.24570 #assigning all night time respiration gap fill night NEE post compost
all_data$all_post_data$RECO_treatment_FIN[(all_data$all_post_data$Rg <= 10 )]<-
all_data$all_post_data$NEE_filled_treatment_mean [(all_data$all_post_data$Rg <= 10 )]
plot(all_data$all_post_data$RECO_treatment_FIN, ylim = c(0,5), col = "blue" )
par(new= TRUE)plot(all_data$all_post_data$RECO_predict_treatment_mean, ylim = c(0,5), col = "red")
summary(all_data$all_post_data$RECO_treatment_FIN) Min. 1st Qu. Median Mean 3rd Qu. Max.
-10.379 1.355 2.084 2.087 2.716 14.635 summary(all_data$all_post_data$RECO_predict_treatment_mean) Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.4264 1.5238 2.1066 2.0869 2.6621 4.1923 summary(all_data$all_post_data$NEE_filled_treatment_mean) Min. 1st Qu. Median Mean 3rd Qu. Max.
-16.46480 -2.23401 0.98957 0.02082 2.16124 14.63480 #creating 1/2 hour GPP control and treatment side values #sum(nee) = sum(gpp+reco) #sum (nee-reco)= sum (gpp)
######Creating 1/2 hourly GPP variable for post compost##########
#control post compost(Reco predict_control mean or Reco_control_fin)
#Use Reco_predict control mean and force all nighttime GPP to zero.
all_data$all_post_data$GPP_control_mean <- (all_data$all_post_data$NEE_filled_control_mean - all_data$all_post_data$RECO_predict_control_mean)
summary(all_data$all_post_data$GPP_control_mean) Min. 1st Qu. Median Mean 3rd Qu. Max.
-17.043 -2.746 0.000 -1.654 0.000 7.816 plot(all_data$all_post_data$GPP_control_mean)
#treatment post compost
all_data$all_post_data$GPP_treatment_mean<- (all_data$all_post_data$NEE_filled_treatment_mean - all_data$all_post_data$RECO_predict_treatment_mean )
summary(all_data$all_post_data$GPP_treatment_mean) Min. 1st Qu. Median Mean 3rd Qu. Max.
-20.2931 -4.2049 -0.5779 -2.0661 0.0000 11.4460 plot(all_data$all_post_data$GPP_treatment_mean)
#######Make all night time GPP zero############ #Need to do this if using reco_predict!
#night time defined as Rg<= 10
#control post compost
all_data$all_post_data$GPP_control_mean [(all_data$all_post_data$Rg <= 10 )]<-0
plot(all_data$all_post_data$GPP_control_mean)
summary(all_data$all_post_data$GPP_control_mean) Min. 1st Qu. Median Mean 3rd Qu. Max.
-17.043 -2.723 0.000 -1.653 0.000 7.765 #treatment post compost
all_data$all_post_data$GPP_treatment_mean [(all_data$all_post_data$Rg <= 10 )]<-0
##########95% CI for GPP#####################
#making 95% confidence intervals for GPP control post compost
all_data$all_post_data$GPP_control_q025 <- (all_data$all_post_data$NEE_predict_control_q025 - all_data$all_post_data$RECO_predict_control_q025)
all_data$all_post_data$GPP_control_q975 <- (all_data$all_post_data$NEE_predict_control_q975 - all_data$all_post_data$RECO_predict_control_q975)
#making 95% confidence intervals for GPP treatment post compost
all_data$all_post_data$GPP_treatment_q025 <- (all_data$all_post_data$NEE_predict_treatment_q025 - all_data$all_post_data$RECO_predict_treatment_q025)
all_data$all_post_data$GPP_treatment_q975 <- (all_data$all_post_data$NEE_predict_treatment_q975 - all_data$all_post_data$RECO_predict_treatment_q975)
##########plotting 1/2hourly Reco and GPP############
#control post compost
plot(all_data$all_post_data$TIMESTAMP[(all_data$all_post_data$Rg > 10 )], all_data$all_post_data$GPP_control_mean[(all_data$all_post_data$Rg > 10 )], col='green', ylim = c(-12,6))
par(new=TRUE)
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$RECO_predict_control_mean , col='red', ylim = c(-12,6)
)
par(new=TRUE)
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_control_mean , ylim = c(-12,6))
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$GPP_control_mean, ylim = c(-12,6))
#treatment post compost
plot(all_data$all_post_data$TIMESTAMP[(all_data$all_post_data$Rg > 10 )], all_data$all_post_data$GPP_treatment_mean[(all_data$all_post_data$Rg > 10 )], col='green', ylim = c(-12,6))
par(new=TRUE)
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$RECO_predict_treatment_mean , col='red', ylim = c(-12,6)
)
par(new=TRUE)
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_treatment_mean , ylim = c(-12,6))
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$GPP_treatment_mean, ylim = c(-12,6))
#GPP pre compost variable
######Creating 1/2 hourly GPP variable##########
#control pre compost
all_data$all_pre_data$GPP_control_mean <- (all_data$all_pre_data$NEE_filled_control_mean - all_data$all_pre_data$RECO_predict_control_mean)
summary(all_data$all_pre_data$GPP_control_mean) Min. 1st Qu. Median Mean 3rd Qu. Max.
-21.990 -6.307 0.000 -3.082 0.000 10.378 #treatment pre compost
all_data$all_pre_data$GPP_treatment_mean<- (all_data$all_pre_data$NEE_filled_treatment_mean - all_data$all_pre_data$RECO_predict_treatment_mean )
summary(all_data$all_pre_data$GPP_treatment_mean) Min. 1st Qu. Median Mean 3rd Qu. Max.
-22.3992 -6.5691 -0.5098 -3.0324 0.0000 14.6568 #######Make all night time GPP zero############ Need to do this since using Reco_predict_mean
#night time defined as Rg<= 10
# #control pre compost
all_data$all_pre_data$GPP_control_mean [(all_data$all_pre_data$Rg <= 10 )]<-0
# #treatment pre compost
all_data$all_pre_data$GPP_treatment_mean [(all_data$all_pre_data$Rg <= 10 )]<-0
##########95% CI for GPP#####################
#making 95% confidence intervals for GPP control pre compost
all_data$all_pre_data$GPP_control_q025 <- (all_data$all_pre_data$NEE_predict_control_q025 - all_data$all_pre_data$RECO_predict_control_q025)
all_data$all_pre_data$GPP_control_q975 <- (all_data$all_pre_data$NEE_predict_control_q975 - all_data$all_pre_data$RECO_predict_control_q975)
#making 95% confidence intervals for GPP treatment pre compost
all_data$all_pre_data$GPP_treatment_q025 <- (all_data$all_pre_data$NEE_predict_treatment_q025 - all_data$all_pre_data$RECO_predict_treatment_q025)
all_data$all_pre_data$GPP_treatment_q975 <- (all_data$all_pre_data$NEE_predict_treatment_q975 - all_data$all_pre_data$RECO_predict_treatment_q975)#Basic plots of control side post compost to make sure I’m on right track before making final figures
#############Cumulative GPP, RECO , NEE control side post compost growing season#####
#cumulative respiration control side
plot(cumsum(tapply(all_data$all_post_data$RECO_predict_control_mean,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
xaxt='n',
xlab='Days of the growing season',
ylab=expression(Cumulative~Reco~GPP~NEE~'('~g~C~m^{-2}~')'),
main='Post Compost Control Side Cumulative RECO, GPP, NEE',
cex.lab = 0.8,
ylim = c(-800,800),
lty=1,
col="red",
lwd=2,
type="l")
axis(side=1,at=seq(0,360,by=30))
par(new = TRUE)
plot(cumsum(tapply(all_data$all_post_data$RECO_predict_control_q975 ,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
xaxt='n',
xlab='Days of the growing season',
ylab=expression(Cumulative~Reco~GPP~NEE~'('~g~C~m^{-2}~')'),
main='Post Compost Control Side Cumulative RECO, GPP, NEE',
cex.lab = 0.8,
ylim = c(-800,800),
lty=2,
col="red",
lwd=2,
type="l")axis(side=1,at=seq(0,360,by=30))
par(new = TRUE)plot(cumsum(tapply(all_data$all_post_data$RECO_predict_control_q025 ,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
xaxt='n',
xlab='Days of the growing season',
ylab=expression(Cumulative~Reco~GPP~NEE~'('~g~C~m^{-2}~')'),
main='Post Compost Control Side Cumulative RECO, GPP, NEE',
cex.lab = 0.8,
ylim = c(-800,800),
lty=2,
col="red",
lwd=2,
type="l")
axis(side=1,at=seq(0,360,by=30))
par(new = TRUE)
#cumulative GPP control side
plot(cumsum(tapply(all_data$all_post_data$GPP_control_mean,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
xaxt='n',
xlab='',
ylab='',
main='',
ylim = c(-800,800),
lty=1,
col="green",
lwd=2,
type="l")axis(side=1,at=seq(0,360,by=30))
par(new = TRUE)
plot(cumsum(tapply(all_data$all_post_data$GPP_control_q025,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
xaxt='n',
xlab='',
ylab='',
main='',
ylim = c(-800,800),
lty=2,
col="green",
lwd=2,
type="l")
axis(side=1,at=seq(0,360,by=30))
par(new = TRUE)
plot(cumsum(tapply(all_data$all_post_data$GPP_control_q975 ,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
xaxt='n',
xlab='',
ylab='',
main='',
ylim = c(-800,800),
lty=2,
col="green",
lwd=2,
type="l")axis(side=1,at=seq(0,360,by=30))
par(new = TRUE)
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_cum_control_mean,
xaxt='n',
xlab='',
ylab='',
main='',
ylim = c(-800,800),
lty=1,
col="grey",
lwd=2,
type="l")
axis(side=1,at=seq(0,360,by=30))
par(new =TRUE)
abline(h = 0, col = "black")
#############Cumulative GPP, RECO , NEE treatmett side post compost growing season#####
#cumulative respiration treatment side
plot(cumsum(tapply(all_data$all_post_data$RECO_predict_treatment_mean,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
xaxt='n',
xlab='Days of the growing season',
ylab=expression(Cumulative~Reco~GPP~NEE~'('~g~C~m^{-2}~')'),
main='Post Compost Treatment Side Cumulative RECO, GPP, NEE',
cex.lab = 0.8,
ylim = c(-800,800),
lty=1,
col="red",
lwd=2,
type="l")
axis(side=1,at=seq(0,360,by=30))
par(new = TRUE)
plot(cumsum(tapply(all_data$all_post_data$RECO_predict_treatment_q975,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
xaxt='n',
xlab='Days of the growing season',
ylab=expression(Cumulative~Reco~GPP~NEE~'('~g~C~m^{-2}~')'),
main='Post Compost Treatment Side Cumulative RECO, GPP, NEE',
cex.lab = 0.8,
ylim = c(-800,800),
lty=2,
col="red",
lwd=2,
type="l")axis(side=1,at=seq(0,360,by=30))
par(new = TRUE)plot(cumsum(tapply(all_data$all_post_data$RECO_predict_treatment_q025,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
xaxt='n',
xlab='Days of the growing season',
ylab=expression(Cumulative~Reco~GPP~NEE~'('~g~C~m^{-2}~')'),
main='Post Compost Treatment Side Cumulative RECO, GPP, NEE',
cex.lab = 0.8,
ylim = c(-800,800),
lty=2,
col="red",
lwd=2,
type="l")
axis(side=1,at=seq(0,360,by=30))
par(new = TRUE)
#cumulative GPP treatment side
plot(cumsum(tapply(all_data$all_post_data$GPP_treatment_mean,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
xaxt='n',
xlab='',
ylab='',
main='',
ylim = c(-800,800),
lty=1,
col="green",
lwd=2,
type="l")axis(side=1,at=seq(0,360,by=30))
par(new = TRUE)
plot(cumsum(tapply(all_data$all_post_data$GPP_treatment_q025 ,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
xaxt='n',
xlab='',
ylab='',
main='',
ylim = c(-800,800),
lty=2,
col="green",
lwd=2,
type="l")
axis(side=1,at=seq(0,360,by=30))
par(new = TRUE)
plot(cumsum(tapply(all_data$all_post_data$GPP_treatment_q975,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
xaxt='n',
xlab='',
ylab='',
main='',
ylim = c(-800,800),
lty=2,
col="green",
lwd=2,
type="l")axis(side=1,at=seq(0,360,by=30))
par(new = TRUE)
#cumulative NEE treatment side
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_cum_treatment_mean,
xaxt='n',
xlab='',
ylab='',
main='',
ylim = c(-800,800),
lty=1,
col="grey",
lwd=2,
type="l")
axis(side=1,at=seq(0,360,by=30))
par(new =TRUE)
abline(h = 0, col = "black")
#blank
all_data$all_post_data$blank = (all_data$all_post_data$RECO_control_FIN *
0)
all_data$all_post_data$blank[(all_data$all_post_data$blank == 0)] <-
NA#Plotting NEE variables###
####panel 1 half-hourly non-gap filled and half hourly gap filled### #panel 1/2 hourly GPP_treatment_mean and Respiration #panel 3 cumulative sums of NEE, Respiration and neg GPP
#Treatment side 1/2 hourly
summary (all_data$all_pre_data$NEE) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-17.931 -2.569 1.230 0.230 3.127 18.246 4117 summary(all_data$all_post_data$NEE) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-16.4648 -1.8643 0.8140 0.0759 2.1964 14.6348 2230 head(all_data$all_pre_data$TIMESTAMP)[1] "2019-11-01 00:00:00 +08" "2019-11-01 00:30:00 +08" "2019-11-01 01:00:00 +08" "2019-11-01 01:30:00 +08"
[5] "2019-11-01 02:00:00 +08" "2019-11-01 02:30:00 +08"nrow(all_data$all_pre_data$NEE_predict_control_mean
)NULL
col.code3 <- list(col.name = c("control (modeled)", "treatment (modeled)",
"control (observed)", "treatment (observed)"),
col = c("firebrick1", "deepskyblue", "firebrick4", "deepskyblue4"))
#figure for cumulative and filled NEE and Reco
target.plot.var_nee <- c("NEE_filled_treatment_mean",
"GPP_treatment_mean",
"blank")
target.plot.var_nee.title <- c(
expression(FC ~ '(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
expression(GPP ~ ';' ~ Reco~'(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
expression(Cumulative~sum~ '(' ~ g ~ C ~ m ^ {-2 } ~ ')')
)
## locate the start of each month
month.loc <- which(
all_data$all_post_data$TIMESTAMP$mday == 1 &
all_data$all_post_data$TIMESTAMP$hour == 0 &
all_data$all_post_data$TIMESTAMP$min == 0
)
month.ticks <-
substr(seq(
all_data$all_post_data$TIMESTAMP[month.loc[1]],
all_data$all_post_data$TIMESTAMP[month.loc[length(month.loc)]],
by = "months"
), 6, 7)
## daily average values
daily_nee.tmp <-
data.frame(
date = tapply(
all_data$all_post_data$time.id,
all_data$all_post_data$Doy_water,
min
),
daily_nee = tapply(
all_data$all_post_data$NEE_filled_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp = tapply(
all_data$all_post_data$GPP_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco = tapply(
all_data$all_post_data$RECO_predict_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
)
)
## convert NEE, RECO_predict_treatment_mean gpp to cumulative sum of carbon
# convert to cumulative carbon
for(ll in 2:4) {
# convert to daily units
daily_nee.tmp[, ll] <-
daily_nee.tmp[, ll] * 12 / 1000000 * 1800 * 48
daily_nee.tmp[is.na(daily_nee.tmp[, ll]), ll] <- 0
# calculate cumulative sum, hard-coded with first/second years
daily_nee.tmp[1:365, ll] <-
cumsum(daily_nee.tmp[1:365, ll])
daily_nee.tmp[366:nrow(daily_nee.tmp), ll] <-
cumsum(daily_nee.tmp[366:nrow(daily_nee.tmp), ll])
# set break (missing value) between two years
daily_nee.tmp[366, ll] <- NA
}
## begin plot
png(
paste0(out.path, "NEE_treatment_growing_post_compost_concord",
all_data$all_post_data$TIMESTAMP$year[1] + 1900, "_",
all_data$all_post_data$TIMESTAMP$yday[1] + 1, "_",
all_data$all_post_data$TIMESTAMP$year[nrow(all_data$all_post_data)] + 1900, "_",
all_data$all_post_data$TIMESTAMP$yday[nrow(all_data$all_post_data)] + 1, "_",
"NEE_",
Sys.Date(), ".png"
),
width = 5.5,
height = 6,
units = "in",
res = 300,
pointsize = 11,
bg = "white"
)
par(oma = c(4, 4.5, 0.5, 0.5),
mar = c(0, 0, 0.25, 0))
par(fig = c(0, 1, 2 / 3, 1), new = FALSE)
plot(
all_data[[2]]$TIMESTAMP,
all_data[[2]]$NEE_filled_treatment_mean,
pch = 20,
cex = 0.7,
col = col.code3$col[1],
las = 1,
ylab = "",
xlab = "",
xaxt = "n",
xaxs = "i",
#yaxt = "n",
ylim = c(-20, 20),
cex.axis = 0.8
)
points(
all_data[[2]]$TIMESTAMP[all_data[[2]]$treatment == "treatment_post_compost"],
all_data[[2]]$NEE[all_data[[2]]$treatment == "treatment_post_compost"],
pch = 20,
cex = 0.7,
col = col.code3$col[4]
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")
text(
x = all_data$all_post_data$time.id[1],
y = 30,
paste0("(a)"),
adj = c(0, 1),
cex = 0.9,
)
mtext(
side = 2,
target.plot.var_nee.title[[1]],
line = 3,
outer = FALSE,
cex = 0.8
)
## panel b
par(fig = c(0, 1, 1 / 3, 2 / 3), new = TRUE)
plot(
all_data$all_post_data$time.id,
all_data$all_post_data$GPP_treatment_mean,
xlab = "",
ylab = "",
cex = 0.5,
col = "forestgreen",
bg = "forestgreen",
xaxt = "n",
las = 1,
pch = 21,
xaxs = "i",
ylim = c(-10, 10),
cex.axis = 0.8
)
points(
all_data$all_post_data$time.id,
all_data$all_post_data$RECO_predict_treatment_mean,
cex = 0.5,
col = "red",
bg = "red",
pch = 21,
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")
text(
x = all_data$all_post_data$time.id[1],
y = 30,
paste0("(b)"),
adj = c(0, 1),
cex = 0.9
)
mtext(
side = 2,
target.plot.var_nee.title[[2]],
line = 3,
outer = FALSE,
cex = 0.8
)
## panel c
par(fig = c(0, 1, 0, 1 / 3), new = TRUE)
plot(
daily_nee.tmp$date,
daily_nee.tmp$daily_nee,
type = "l",
lwd = 1.5,
col = "black",
xlab = "",
ylab = "",
xaxt = "n",
las = 1,
xaxs = "i",
ylim = c(-375, 375),
cex.axis = 0.8
)
lines(daily_nee.tmp$date,
daily_nee.tmp$daily_gpp,
lwd = 1.5,
col = "forestgreen",
lty = 2
)
lines(daily_nee.tmp$date,
daily_nee.tmp$daily_reco,
lwd = 1.5,
col = "red",
lty = 3
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")
axis(
side = 1,
at = all_data$all_post_data$time.id[month.loc],
labels = month.ticks,
tck = -.025,
cex.axis = 0.8
)
text(
x = all_data$all_post_data$time.id[1],
y = 1200,
paste0("(c)"),
adj = c(0, 1),
cex = 0.9
)
mtext(
side = 2,
target.plot.var_nee.title[[3]],
line = 3,
outer = FALSE,
cex = 0.8
)
axis(
side = 1,
at = c(2019.75, 2020.5, 2021.17),
label = c(2019, 2020, 2021),
cex.axis = 0.8,
tck = -.025,
lty = 0,
bty = "n",
line = 0.9
)
mtext(
side = 1,
"Month / Year",
line = 3,
outer = FALSE,
cex = 0.8
)
dev.off()null device
1 ####panel 1 half-hourly non-gap filled and half hourly gap filled### #panel 2 daily GPP_treatment_mean and Respiration with error lines shaded #panel 3 cumulative sums of NEE, Respiration and neg GPP with error lines
#Treatment side Daily
col.code4 <- list(col.name = c( "treatment (modeled)","treatment (observed)"),
col = c("deepskyblue", "deepskyblue4"))
col.code5 <- list(col.name = c("Reco", "GPP"),
col3 = c("lightcoral", "lightgreen"))
#figure for cumulative and filled NEE and Reco
target.plot.var_nee <- c("NEE_filled_treatment_mean",
"GPP_treatment_mean",
"blank")
target.plot.var_nee.title <- c(
expression(FC ~ '(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
expression(GPP ~ ';' ~ Reco~'(' ~ mu ~ mol ~ m ^ {-2 } ~ d ^ { -1 } ~ ')'),
expression(Cumulative~sum~ '(' ~ g ~ C ~ m ^ {-2 } ~ ')')
)
## locate the start of each month
month.loc <- which(
all_data$all_post_data$TIMESTAMP$mday == 1 &
all_data$all_post_data$TIMESTAMP$hour == 0 &
all_data$all_post_data$TIMESTAMP$min == 0
)
month.ticks <-
substr(seq(
all_data$all_post_data$TIMESTAMP[month.loc[1]],
all_data$all_post_data$TIMESTAMP[month.loc[length(month.loc)]],
by = "months"
), 6, 7)
## daily average values
daily_nee.tmp <-
data.frame(
date = tapply(
all_data$all_post_data$time.id,
all_data$all_post_data$Doy_water,
min
),
daily_nee = tapply(
all_data$all_post_data$NEE_filled_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp = tapply(
all_data$all_post_data$GPP_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco = tapply(
all_data$all_post_data$RECO_predict_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp_q025 = tapply(
all_data$all_post_data$GPP_treatment_q025,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gppq975 = tapply(
all_data$all_post_data$GPP_treatment_q975,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco025 = tapply(
all_data$all_post_data$RECO_predict_treatment_q025,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco975 = tapply(
all_data$all_post_data$RECO_predict_treatment_q975,
all_data$all_post_data$Doy_water,
na.mean
)
)
#Creating a second daily value data frame that is not cum_sum for panel B
daily_nee.tmp_2 <-
data.frame(
date = tapply(
all_data$all_post_data$time.id,
all_data$all_post_data$Doy_water,
min
),
daily_nee = tapply(
all_data$all_post_data$NEE_filled_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp = tapply(
all_data$all_post_data$GPP_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco = tapply(
all_data$all_post_data$RECO_predict_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp_q025 = tapply(
all_data$all_post_data$GPP_treatment_q025,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gppq975 = tapply(
all_data$all_post_data$GPP_treatment_q975,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco025 = tapply(
all_data$all_post_data$RECO_predict_treatment_q025,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco975 = tapply(
all_data$all_post_data$RECO_predict_treatment_q975,
all_data$all_post_data$Doy_water,
na.mean
)
)
## convert NEE, RECO_predict_treatment_mean gpp to cumulative sum of carbon
# convert to cumulative carbon
for(ll in 2:8) {
# convert to daily units
daily_nee.tmp[, ll] <-
daily_nee.tmp[, ll] * 12 / 1000000 * 1800 * 48
daily_nee.tmp[is.na(daily_nee.tmp[, ll]), ll] <- 0
# calculate cumulative sum, hard-coded with first/second years
daily_nee.tmp[1:365, ll] <-
cumsum(daily_nee.tmp[1:365, ll])
daily_nee.tmp[366:nrow(daily_nee.tmp), ll] <-
cumsum(daily_nee.tmp[366:nrow(daily_nee.tmp), ll])
# set break (missing value) between two years
daily_nee.tmp[366, ll] <- NA
}
## begin plot
png(
paste0(out.path, "NEE_treatment_Daily_growing_post_compost_concord",
all_data$all_post_data$TIMESTAMP$year[1] + 1900, "_",
all_data$all_post_data$TIMESTAMP$yday[1] + 1, "_",
all_data$all_post_data$TIMESTAMP$year[nrow(all_data$all_post_data)] + 1900, "_",
all_data$all_post_data$TIMESTAMP$yday[nrow(all_data$all_post_data)] + 1, "_",
"NEE_",
Sys.Date(), ".png"
),
width = 5.5,
height = 6,
units = "in",
res = 300,
pointsize = 11,
bg = "white"
)
par(oma = c(4, 4.5, 0.5, 0.5),
mar = c(0, 0, 0.25, 0))
par(fig = c(0, 1, 2 / 3, 1), new = FALSE)
plot(
all_data[[2]]$TIMESTAMP,
all_data[[2]]$NEE_filled_treatment_mean,
pch = 20,
cex = 0.7,
col = col.code4$col[1],
las = 1,
ylab = "",
xlab = "",
xaxt = "n",
xaxs = "i",
#yaxt = "n",
ylim = c(-15, 15),
cex.axis = 0.8
)
points(
all_data[[2]]$TIMESTAMP[all_data[[2]]$treatment == "treatment_post_compost"],
all_data[[2]]$NEE[all_data[[2]]$treatment == "treatment_post_compost"],
pch = 20,
cex = 0.7,
col = col.code4$col[2]
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")
text(
x = all_data$all_post_data$time.id[1],
y = 30,
paste0("(a)"),
adj = c(0, 1),
cex = 0.9,
)
mtext(
side = 2,
target.plot.var_nee.title[[1]],
line = 3,
outer = FALSE,
cex = 0.8
)
legend(
"topleft",
fill = col.code4$col,
border = NA,
legend = col.code4$col.name,
ncol = 2,
cex = 0.7,
bty = "n"
)
## panel b
par(fig = c(0, 1, 1 / 3, 2 / 3), new = TRUE)
plot(
daily_nee.tmp_2$date,
daily_nee.tmp_2$daily_gpp,
xlab = "",
ylab = "",
cex = 0,
col = "forestgreen",
bg = "forestgreen",
xaxt = "n",
las = 1,
pch = 21,
xaxs = "i",
ylim = c(-10, 10),
cex.axis = 0.8
)polygon(c(daily_nee.tmp_2$date,
rev(daily_nee.tmp_2$date)),
c(daily_nee.tmp_2$daily_reco975 ,
rev(daily_nee.tmp_2$daily_reco025)),
col = col.code5$col3[1],
border = NA)
polygon(c(daily_nee.tmp_2$date,
rev(daily_nee.tmp_2$date)),
c(daily_nee.tmp_2$daily_gppq975 ,
rev(daily_nee.tmp_2$daily_gpp_q025)),
col = col.code5$col3[2],
border = NA)
points(
daily_nee.tmp_2$date,
daily_nee.tmp_2$daily_reco,
cex = 0.6,
col = "red",
bg = "red",
pch = 21,
)
points(
daily_nee.tmp_2$date,
daily_nee.tmp_2$daily_gpp,
cex = 0.6,
col = "forestgreen",
bg = "forestgreen",
pch = 21,
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")
text(
x = all_data$all_post_data$time.id[1],
y = 30,
paste0("(b)"),
adj = c(0, 1),
cex = 0.9
)
mtext(
side = 2,
target.plot.var_nee.title[[2]],
line = 3,
outer = FALSE,
cex = 0.8
)
## panel c
par(fig = c(0, 1, 0, 1 / 3), new = TRUE)
plot(
daily_nee.tmp$date,
daily_nee.tmp$daily_nee,
type = "l",
lwd = 1.5,
col = "black",
xlab = "",
ylab = "",
xaxt = "n",
las = 1,
xaxs = "i",
ylim = c(-600, 800),
cex.axis = 0.8
)
lines(daily_nee.tmp$date,
daily_nee.tmp$daily_gpp,
lwd = 1.5,
col = "forestgreen",
lty = 1
)
lines(daily_nee.tmp$date,
daily_nee.tmp$daily_reco,
lwd = 1.5,
col = "red",
lty = 1
)
lines(daily_nee.tmp$date,
daily_nee.tmp$daily_gpp_q025 ,
lwd = 1,
col = "forestgreen",
lty = 3
)
lines(daily_nee.tmp$date,
daily_nee.tmp$daily_gppq975 ,
lwd = 1,
col = "forestgreen",
lty = 3
)
lines(daily_nee.tmp$date,
daily_nee.tmp$daily_reco025,
lwd = 1,
col = "red",
lty = 3
)
lines(daily_nee.tmp$date,
daily_nee.tmp$daily_reco975,
lwd = 1,
col = "red",
lty = 3
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")
axis(
side = 1,
at = all_data$all_post_data$time.id[month.loc],
labels = month.ticks,
tck = -.025,
cex.axis = 0.8
)
text(
x = all_data$all_post_data$time.id[1],
y = 1200,
paste0("(c)"),
adj = c(0, 1),
cex = 0.9
)
mtext(
side = 2,
target.plot.var_nee.title[[3]],
line = 3,
outer = FALSE,
cex = 0.8
)
axis(
side = 1,
at = c(2019.75, 2020.92, 2021.17),
label = c(2019, 2020, 2021),
cex.axis = 0.8,
tck = -.025,
lty = 0,
bty = "n",
line = 0.9
)
mtext(
side = 1,
"Month / Year",
line = 3,
outer = FALSE,
cex = 0.8
)
dev.off()null device
1 #Appendix figure showing observed and modeled treatment side post compost application
####panel 1 half-hourly non-gap filled and half hourly gap filled### #panel 2 daily GPP_treatment_mean and Respiration with error lines shaded
col.code4 <- list(col.name = c( "treatment (modeled)","treatment (observed)"),
col = c("deepskyblue", "deepskyblue4"))
col.code5 <- list(col.name = c("Reco", "GPP"),
col3 = c("lightcoral", "lightgreen"))
#figure for cumulative and filled NEE and Reco
target.plot.var_nee <- c("NEE_filled_treatment_mean",
"GPP_treatment_mean",
"blank")
target.plot.var_nee.title <- c(
expression(FC ~ '(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
expression(GPP ~ ';' ~ Reco~'(' ~ mu ~ mol ~ m ^ {-2 } ~ d ^ { -1 } ~ ')'),
expression(Cumulative~sum~ '(' ~ g ~ C ~ m ^ {-2 } ~ ')')
)
## locate the start of each month
month.loc <- which(
all_data$all_post_data$TIMESTAMP$mday == 1 &
all_data$all_post_data$TIMESTAMP$hour == 0 &
all_data$all_post_data$TIMESTAMP$min == 0
)
month.ticks <-
substr(seq(
all_data$all_post_data$TIMESTAMP[month.loc[1]],
all_data$all_post_data$TIMESTAMP[month.loc[length(month.loc)]],
by = "months"
), 6, 7)
## daily average values
daily_nee.tmp <-
data.frame(
date = tapply(
all_data$all_post_data$time.id,
all_data$all_post_data$Doy_water,
min
),
daily_nee = tapply(
all_data$all_post_data$NEE_filled_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp = tapply(
all_data$all_post_data$GPP_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco = tapply(
all_data$all_post_data$RECO_predict_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp_q025 = tapply(
all_data$all_post_data$GPP_treatment_q025,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gppq975 = tapply(
all_data$all_post_data$GPP_treatment_q975,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco025 = tapply(
all_data$all_post_data$RECO_predict_treatment_q025,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco975 = tapply(
all_data$all_post_data$RECO_predict_treatment_q975,
all_data$all_post_data$Doy_water,
na.mean
)
)
#Creating a second daily value data frame that is not cum_sum for panel B
daily_nee.tmp_2 <-
data.frame(
date = tapply(
all_data$all_post_data$time.id,
all_data$all_post_data$Doy_water,
min
),
daily_nee = tapply(
all_data$all_post_data$NEE_filled_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp = tapply(
all_data$all_post_data$GPP_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco = tapply(
all_data$all_post_data$RECO_predict_treatment_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp_q025 = tapply(
all_data$all_post_data$GPP_treatment_q025,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gppq975 = tapply(
all_data$all_post_data$GPP_treatment_q975,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco025 = tapply(
all_data$all_post_data$RECO_predict_treatment_q025,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco975 = tapply(
all_data$all_post_data$RECO_predict_treatment_q975,
all_data$all_post_data$Doy_water,
na.mean
)
)
## convert NEE, RECO_predict_treatment_mean gpp to cumulative sum of carbon
# convert to cumulative carbon
for(ll in 2:8) {
# convert to daily units
daily_nee.tmp[, ll] <-
daily_nee.tmp[, ll] * 12 / 1000000 * 1800 * 48
daily_nee.tmp[is.na(daily_nee.tmp[, ll]), ll] <- 0
# calculate cumulative sum, hard-coded with first/second years
daily_nee.tmp[1:365, ll] <-
cumsum(daily_nee.tmp[1:365, ll])
daily_nee.tmp[366:nrow(daily_nee.tmp), ll] <-
cumsum(daily_nee.tmp[366:nrow(daily_nee.tmp), ll])
# set break (missing value) between two years
daily_nee.tmp[366, ll] <- NA
}
## begin plot
png(
paste0(out.path, "NEE_treatment_appendix_model_observed_daily",
all_data$all_post_data$TIMESTAMP$year[1] + 1900, "_",
all_data$all_post_data$TIMESTAMP$yday[1] + 1, "_",
all_data$all_post_data$TIMESTAMP$year[nrow(all_data$all_post_data)] + 1900, "_",
all_data$all_post_data$TIMESTAMP$yday[nrow(all_data$all_post_data)] + 1, "_",
"NEE_",
Sys.Date(), ".png"
),
width = 5.5,
height = 6,
units = "in",
res = 300,
pointsize = 11,
bg = "white"
)
par(oma = c(4, 4.5, 0.5, 0.5),
mar = c(0, 0, 0.25, 0))
par(fig = c(0, 1, 1 /2, 1), new = FALSE)
plot(
all_data[[2]]$TIMESTAMP,
all_data[[2]]$NEE_filled_treatment_mean,
pch = 20,
cex = 0.7,
col = col.code4$col[1],
las = 1,
ylab = "",
xlab = "",
xaxt = "n",
xaxs = "i",
#yaxt = "n",
ylim = c(-15, 15),
cex.axis = 0.8
)
points(
all_data[[2]]$TIMESTAMP[all_data[[2]]$treatment == "treatment_post_compost"],
all_data[[2]]$NEE[all_data[[2]]$treatment == "treatment_post_compost"],
pch = 20,
cex = 0.7,
col = col.code4$col[2]
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")
text(
x = all_data$all_post_data$time.id[1],
y = 30,
paste0("(a)"),
adj = c(0, 1),
cex = 0.9,
)
#adding panel label
mtext(
"A", side=2, line=3, at=14, las = 1
)
mtext(
side = 2,
target.plot.var_nee.title[[1]],
line = 3,
outer = FALSE,
cex = 0.8
)
legend(
"topleft",
fill = col.code4$col,
border = NA,
legend = col.code4$col.name,
ncol = 2,
cex = 0.7,
bty = "n"
)
## panel b
par(fig = c(0, 1, 0, 1 / 2), new = TRUE)
plot(
daily_nee.tmp_2$date,
daily_nee.tmp_2$daily_gpp,
xlab = "",
ylab = "",
cex = 0,
col = "forestgreen",
bg = "forestgreen",
xaxt = "n",
las = 1,
pch = 21,
xaxs = "i",
ylim = c(-10, 10),
cex.axis = 0.8
)polygon(c(daily_nee.tmp_2$date,
rev(daily_nee.tmp_2$date)),
c(daily_nee.tmp_2$daily_reco975 ,
rev(daily_nee.tmp_2$daily_reco025)),
col = col.code5$col3[1],
border = NA)
polygon(c(daily_nee.tmp_2$date,
rev(daily_nee.tmp_2$date)),
c(daily_nee.tmp_2$daily_gppq975 ,
rev(daily_nee.tmp_2$daily_gpp_q025)),
col = col.code5$col3[2],
border = NA)
points(
daily_nee.tmp_2$date,
daily_nee.tmp_2$daily_reco,
cex = 0.6,
col = "red",
bg = "red",
pch = 21,
)
points(
daily_nee.tmp_2$date,
daily_nee.tmp_2$daily_gpp,
cex = 0.6,
col = "forestgreen",
bg = "forestgreen",
pch = 21,
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")
text(
x = all_data$all_post_data$time.id[1],
y = 30,
paste0("(b)"),
adj = c(0, 1),
cex = 0.9
)
mtext(
side = 2,
target.plot.var_nee.title[[2]],
line = 3,
outer = FALSE,
cex = 0.8
)
axis(
side = 1,
at = all_data$all_post_data$time.id[month.loc],
labels = month.ticks,
tck = -.025,
cex.axis = 0.8
)
text(
x = all_data$all_post_data$time.id[1],
y = 1200,
paste0("(c)"),
adj = c(0, 1),
cex = 0.9
)
axis(
side = 1,
at = c(2019.75, 2020.92, 2021.17),
label = c(2019, 2020, 2021),
cex.axis = 0.8,
tck = -.025,
lty = 0,
bty = "n",
line = 0.9
)
mtext(
side = 1,
"Month / Year",
line = 3,
outer = FALSE,
cex = 0.8
)
mtext(
"B", side=2, line=3, at=9, las = 1
)
dev.off()null device
1 ####panel 1 Control half-hourly non-gap filled and half hourly gap filled### #panel 2 1/2 hourly GPP_control_mean and Respiration with error lines shaded #panel 3 cumulative sums of Control NEE, Respiration and GPP
#Control side side with 1/2 hourly data in panel 2
#figure for cumulative and filled NEE and Reco
target.plot.var_nee_cont <- c("NEE_filled_control_mean",
"GPP_control_mean",
"blank")
target.plot.var_nee_cont.title <- c(
expression(FC ~ '(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
expression(GPP ~ ';' ~ Reco~'(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
expression(Cumulative~sum~ '(' ~ g ~ C ~ m ^ {-2 } ~ ')')
)
## locate the start of each month
month.loc <- which(
all_data$all_post_data$TIMESTAMP$mday == 1 &
all_data$all_post_data$TIMESTAMP$hour == 0 &
all_data$all_post_data$TIMESTAMP$min == 0
)
month.ticks <-
substr(seq(
all_data$all_post_data$TIMESTAMP[month.loc[1]],
all_data$all_post_data$TIMESTAMP[month.loc[length(month.loc)]],
by = "months"
), 6, 7)
## daily average values
daily_nee.tmp_cont <-
data.frame(
date = tapply(
all_data$all_post_data$time.id,
all_data$all_post_data$Doy_water,
min
),
daily_nee = tapply(
all_data$all_post_data$NEE_filled_control_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp = tapply(
all_data$all_post_data$GPP_control_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco = tapply(
all_data$all_post_data$RECO_predict_control_mean,
all_data$all_post_data$Doy_water,
na.mean
)
)
## convert NEE, RECO_predict_treatment_mean gpp to cumulative sum of carbon
# convert to cumulative carbon
for(ll in 2:4) {
# convert to daily units
daily_nee.tmp_cont[, ll] <-
daily_nee.tmp_cont[, ll] * 12 / 1000000 * 1800 * 48
daily_nee.tmp_cont[is.na(daily_nee.tmp_cont[, ll]), ll] <- 0
# calculate cumulative sum, hard-coded with first/second years
daily_nee.tmp_cont[1:365, ll] <-
cumsum(daily_nee.tmp_cont[1:365, ll])
daily_nee.tmp_cont[366:nrow(daily_nee.tmp_cont), ll] <-
cumsum(daily_nee.tmp_cont[366:nrow(daily_nee.tmp_cont), ll])
# set break (missing value) between two years
daily_nee.tmp_cont[366, ll] <- NA
}
## begin plot
png(
paste0(out.path, "NEE_CONTROL_growing_post_compost_concord",
all_data$all_post_data$TIMESTAMP$year[1] + 1900, "_",
all_data$all_post_data$TIMESTAMP$yday[1] + 1, "_",
all_data$all_post_data$TIMESTAMP$year[nrow(all_data$all_post_data)] + 1900, "_",
all_data$all_post_data$TIMESTAMP$yday[nrow(all_data$all_post_data)] + 1, "_",
"NEE_",
Sys.Date(), ".png"
),
width = 5.5,
height = 6,
units = "in",
res = 300,
pointsize = 11,
bg = "white"
)
par(oma = c(4, 4.5, 0.5, 0.5),
mar = c(0, 0, 0.25, 0))
par(fig = c(0, 1, 2 / 3, 1), new = FALSE)
plot(
all_data[[2]]$TIMESTAMP,
all_data[[2]]$NEE_filled_treatment_mean,
pch = 20,
cex = 0.7,
col = col.code4$col[1],
las = 1,
ylab = "",
xlab = "",
xaxt = "n",
xaxs = "i",
#yaxt = "n",
ylim = c(-20, 20),
cex.axis = 0.8
)
points(
all_data[[2]]$TIMESTAMP[all_data[[2]]$treatment == "control_post_compost"],
all_data[[2]]$NEE[all_data[[2]]$treatment == "control_post_compost"],
pch = 20,
cex = 0.7,
col = col.code4$col[4]
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp_cont$date[366], lwd= 1.5, col = "black")
text(
x = all_data$all_post_data$time.id[1],
y = 30,
paste0("(a)"),
adj = c(0, 1),
cex = 0.9
)
mtext(
side = 2,
target.plot.var_nee_cont.title[[1]],
line = 3,
outer = FALSE,
cex = 0.8
)
## panel b
par(fig = c(0, 1, 1 / 3, 2 / 3), new = TRUE)
plot(
all_data$all_post_data$time.id,
all_data$all_post_data$GPP_control_mean,
xlab = "",
ylab = "",
cex = 0.5,
col = "forestgreen",
bg = "forestgreen",
xaxt = "n",
las = 1,
pch = 21,
xaxs = "i",
ylim = c(-10, 10),
cex.axis = 0.8
)
points(
all_data$all_post_data$time.id,
all_data$all_post_data$RECO_predict_control_mean,
cex = 0.5,
col = "red",
bg = "red",
pch = 21,
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp_cont$date[366], lwd= 1.5, col = "black")
text(
x = all_data$all_post_data$time.id[1],
y = 30,
paste0("(b)"),
adj = c(0, 1),
cex = 0.9
)
mtext(
side = 2,
target.plot.var_nee_cont.title[[2]],
line = 3,
outer = FALSE,
cex = 0.8
)
## panel c
par(fig = c(0, 1, 0, 1 / 3), new = TRUE)
plot(
daily_nee.tmp_cont$date,
daily_nee.tmp_cont$daily_nee,
type = "l",
lwd = 1.5,
col = "black",
xlab = "",
ylab = "",
xaxt = "n",
las = 1,
xaxs = "i",
ylim = c(-375, 375),
cex.axis = 0.8
)
lines(daily_nee.tmp_cont$date,
daily_nee.tmp_cont$daily_gpp,
lwd = 1.5,
col = "forestgreen",
lty = 2
)
lines(daily_nee.tmp_cont$date,
daily_nee.tmp_cont$daily_reco,
lwd = 1.5,
col = "red",
lty = 3
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp_cont$date[366], lwd= 1.5, col = "black")
axis(
side = 1,
at = all_data$all_post_data$time.id[month.loc],
labels = month.ticks,
tck = -.025,
cex.axis = 0.8
)
text(
x = all_data$all_post_data$time.id[1],
y = 1200,
paste0("(c)"),
adj = c(0, 1),
cex = 0.9
)
mtext(
side = 2,
target.plot.var_nee_cont.title[[3]],
line = 3,
outer = FALSE,
cex = 0.8
)
axis(
side = 1,
at = c(2019.75, 2020.5, 2021.17),
label = c(2019, 2020, 2021),
cex.axis = 0.8,
tck = -.025,
lty = 0,
bty = "n",
line = 0.9
)
mtext(
side = 1,
"Month / Year",
line = 3,
outer = FALSE,
cex = 0.8
)
dev.off()null device
1 ####panel 1 Control half-hourly non-gap filled and half hourly gap filled### #panel 2 daily GPP_control_mean and Respiration with error lines shaded #panel 3 cumulative sums of Control NEE, Respiration and neg GPP with error lines
#control side daily data in panel 2
col.code6 <- list(col.name = c( "control (modeled)","control (observed)"),
col = c("firebrick1", "firebrick4"))
#figure for cumulative and filled NEE and Reco
target.plot.var_nee_control_daily <- c("NEE_filled_control_mean",
"GPP_control_mean",
"blank")
target.plot.var_nee_control_daily.title <- c(
expression(FC ~ '(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
expression(GPP ~ ';' ~ Reco~'(' ~ mu ~ mol ~ m ^ {-2 } ~ d ^ { -1 } ~ ')'),
expression(Cumulative~sum~ '(' ~ g ~ C ~ m ^ {-2 } ~ ')')
)
## locate the start of each month
month.loc <- which(
all_data$all_post_data$TIMESTAMP$mday == 1 &
all_data$all_post_data$TIMESTAMP$hour == 0 &
all_data$all_post_data$TIMESTAMP$min == 0
)
month.ticks <-
substr(seq(
all_data$all_post_data$TIMESTAMP[month.loc[1]],
all_data$all_post_data$TIMESTAMP[month.loc[length(month.loc)]],
by = "months"
), 6, 7)
## daily average values
daily_nee.tmp_controlside <-
data.frame(
date = tapply(
all_data$all_post_data$time.id,
all_data$all_post_data$Doy_water,
min
),
daily_nee = tapply(
all_data$all_post_data$NEE_filled_control_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp = tapply(
all_data$all_post_data$GPP_control_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco = tapply(
all_data$all_post_data$RECO_predict_control_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp_q025 = tapply(
all_data$all_post_data$GPP_control_q025,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gppq975 = tapply(
all_data$all_post_data$GPP_control_q975,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco025 = tapply(
all_data$all_post_data$RECO_predict_control_q025,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco975 = tapply(
all_data$all_post_data$RECO_predict_control_q975,
all_data$all_post_data$Doy_water,
na.mean
)
)
#Creating a second daily value data frame that is not cum_sum for panel B
daily_nee.tmp_controlside_2 <-
data.frame(
date = tapply(
all_data$all_post_data$time.id,
all_data$all_post_data$Doy_water,
min
),
daily_nee = tapply(
all_data$all_post_data$NEE_filled_control_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp = tapply(
all_data$all_post_data$GPP_control_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco = tapply(
all_data$all_post_data$RECO_predict_control_mean,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gpp_q025 = tapply(
all_data$all_post_data$GPP_control_q025,
all_data$all_post_data$Doy_water,
na.mean
),
daily_gppq975 = tapply(
all_data$all_post_data$GPP_control_q975,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco025 = tapply(
all_data$all_post_data$RECO_predict_control_q025,
all_data$all_post_data$Doy_water,
na.mean
),
daily_reco975 = tapply(
all_data$all_post_data$RECO_predict_control_q975,
all_data$all_post_data$Doy_water,
na.mean
)
)
## convert NEE, RECO_predict_control_mean gpp to cumulative sum of carbon
# convert to cumulative carbon
for(ll in 2:8) {
# convert to daily units
daily_nee.tmp_controlside[, ll] <-
daily_nee.tmp_controlside[, ll] * 12 / 1000000 * 1800 * 48
daily_nee.tmp_controlside[is.na(daily_nee.tmp_controlside[, ll]), ll] <- 0
# calculate cumulative sum, hard-coded with first/second years
daily_nee.tmp_controlside[1:365, ll] <-
cumsum(daily_nee.tmp_controlside[1:365, ll])
daily_nee.tmp_controlside[366:nrow(daily_nee.tmp_controlside), ll] <-
cumsum(daily_nee.tmp_controlside[366:nrow(daily_nee.tmp_controlside), ll])
# set break (missing value) between two years
daily_nee.tmp_controlside[366, ll] <- NA
}
## begin plot
png(
paste0(out.path, "NEE_control_Daily_growing_post_compost_concord",
all_data$all_post_data$TIMESTAMP$year[1] + 1900, "_",
all_data$all_post_data$TIMESTAMP$yday[1] + 1, "_",
all_data$all_post_data$TIMESTAMP$year[nrow(all_data$all_post_data)] + 1900, "_",
all_data$all_post_data$TIMESTAMP$yday[nrow(all_data$all_post_data)] + 1, "_",
"NEE_",
Sys.Date(), ".png"
),
width = 5.5,
height = 6,
units = "in",
res = 300,
pointsize = 11,
bg = "white"
)
par(oma = c(4, 4.5, 0.5, 0.5),
mar = c(0, 0, 0.25, 0))
par(fig = c(0, 1, 2 / 3, 1), new = FALSE)
plot(
all_data[[2]]$TIMESTAMP,
all_data[[2]]$NEE_filled_control_mean,
pch = 20,
cex = 0.7,
col = col.code6$col[1],
las = 1,
ylab = "",
xlab = "",
xaxt = "n",
xaxs = "i",
#yaxt = "n",
ylim = c(-15, 15),
cex.axis = 0.8
)
points(
all_data[[2]]$TIMESTAMP[all_data[[2]]$treatment == "control_post_compost"],
all_data[[2]]$NEE[all_data[[2]]$treatment == "control_post_compost"],
pch = 20,
cex = 0.7,
col = col.code6$col[2]
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp_controlside$date[366], lwd= 1.5, col = "black")
text(
x = all_data$all_post_data$time.id[1],
y = 30,
paste0("(a)"),
adj = c(0, 1),
cex = 0.9,
)
mtext(
side = 2,
target.plot.var_nee_control_daily.title[[1]],
line = 3,
outer = FALSE,
cex = 0.8
)
legend(
"topleft",
fill = col.code6$col,
border = NA,
legend = col.code6$col.name,
ncol = 2,
cex = 0.7,
bty = "n"
)
## panel b
par(fig = c(0, 1, 1 / 3, 2 / 3), new = TRUE)
plot(
daily_nee.tmp_controlside_2$date,
daily_nee.tmp_controlside_2$daily_gpp,
xlab = "",
ylab = "",
cex = 0,
col = "forestgreen",
bg = "forestgreen",
xaxt = "n",
las = 1,
pch = 21,
xaxs = "i",
ylim = c(-10, 10),
cex.axis = 0.8
)polygon(c(daily_nee.tmp_controlside_2$date,
rev(daily_nee.tmp_controlside_2$date)),
c(daily_nee.tmp_controlside_2$daily_reco975 ,
rev(daily_nee.tmp_controlside_2$daily_reco025)),
col = col.code5$col3[1],
border = NA)
polygon(c(daily_nee.tmp_controlside_2$date,
rev(daily_nee.tmp_controlside_2$date)),
c(daily_nee.tmp_controlside_2$daily_gppq975 ,
rev(daily_nee.tmp_controlside_2$daily_gpp_q025)),
col = col.code5$col3[2],
border = NA)
points(
daily_nee.tmp_controlside_2$date,
daily_nee.tmp_controlside_2$daily_reco,
cex = 0.6,
col = "red",
bg = "red",
pch = 21,
)
points(
daily_nee.tmp_controlside_2$date,
daily_nee.tmp_controlside_2$daily_gpp,
cex = 0.6,
col = "forestgreen",
bg = "forestgreen",
pch = 21,
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp_controlside$date[366], lwd= 1.5, col = "black")
text(
x = all_data$all_post_data$time.id[1],
y = 30,
paste0("(b)"),
adj = c(0, 1),
cex = 0.9
)
mtext(
side = 2,
target.plot.var_nee_control_daily.title[[2]],
line = 3,
outer = FALSE,
cex = 0.8
)
## panel c
par(fig = c(0, 1, 0, 1 / 3), new = TRUE)
plot(
daily_nee.tmp_controlside$date,
daily_nee.tmp_controlside$daily_nee,
type = "l",
lwd = 1.5,
col = "black",
xlab = "",
ylab = "",
xaxt = "n",
las = 1,
xaxs = "i",
ylim = c(-600, 800),
cex.axis = 0.8
)
lines(daily_nee.tmp_controlside$date,
daily_nee.tmp_controlside$daily_gpp,
lwd = 1.5,
col = "forestgreen",
lty = 1
)
lines(daily_nee.tmp_controlside$date,
daily_nee.tmp_controlside$daily_reco,
lwd = 1.5,
col = "red",
lty = 1
)
lines(daily_nee.tmp_controlside$date,
daily_nee.tmp_controlside$daily_gpp_q025 ,
lwd = 1,
col = "forestgreen",
lty = 3
)
lines(daily_nee.tmp_controlside$date,
daily_nee.tmp_controlside$daily_gppq975 ,
lwd = 1,
col = "forestgreen",
lty = 3
)
lines(daily_nee.tmp_controlside$date,
daily_nee.tmp_controlside$daily_reco025,
lwd = 1,
col = "red",
lty = 3
)
lines(daily_nee.tmp_controlside$date,
daily_nee.tmp_controlside$daily_reco975,
lwd = 1,
col = "red",
lty = 3
)
abline(
v = 2020 + 290 / 366,
col = "red",
lwd = 2,
lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp_controlside$date[366], lwd= 1.5, col = "black")
axis(
side = 1,
at = all_data$all_post_data$time.id[month.loc],
labels = month.ticks,
tck = -.025,
cex.axis = 0.8
)
text(
x = all_data$all_post_data$time.id[1],
y = 1200,
paste0("(c)"),
adj = c(0, 1),
cex = 0.9
)
mtext(
side = 2,
target.plot.var_nee_control_daily.title[[3]],
line = 3,
outer = FALSE,
cex = 0.8
)
axis(
side = 1,
at = c(2019.75, 2020.92, 2021.17),
label = c(2019, 2020, 2021),
cex.axis = 0.8,
tck = -.025,
lty = 0,
bty = "n",
line = 0.9
)
mtext(
side = 1,
"Month / Year",
line = 3,
outer = FALSE,
cex = 0.8
)
dev.off()null device
1 #Cumlative sum Reco control side pre compost application with CI’s
cumsum(tapply(all_data$all_pre_data$RECO_predict_control_mean,
round(all_data$all_pre_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4 5
1.018558 1.517265 1.933566 2.997678 4.517920
6 7 8 9 10
6.467492 9.070431 12.335729 14.977789 17.351610
11 12 13 14 15
20.202573 23.570593 26.953766 31.553771 37.315138
16 17 18 19 20
42.799904 47.708266 51.926821 56.347299 60.520131
21 22 23 24 25
63.336093 65.712075 68.034734 70.425435 73.017981
26 27 28 29 30
74.966400 77.867475 80.218900 82.335965 84.561258
31 32 33 34 35
87.642691 91.753907 96.172485 100.478985 104.616095
36 37 38 39 40
108.774436 112.941625 117.453755 121.906032 126.496191
41 42 43 44 45
131.073908 135.712040 140.500195 144.881659 148.357128
46 47 48 49 50
150.862489 152.890973 154.771717 157.529570 160.463212
51 52 53 54 55
162.854742 164.915754 166.834201 168.520052 170.208046
56 57 58 59 60
172.069845 173.695339 175.290398 177.360839 179.274880
61 62 63 64 65
181.178295 183.498034 186.230667 188.968437 191.843933
66 67 68 69 70
194.871520 198.024217 200.901187 203.896772 207.028310
71 72 73 74 75
209.745644 212.697463 215.854212 219.022998 221.584351
76 77 78 79 80
224.465189 226.949396 228.705088 231.033237 233.604268
81 82 83 84 85
236.258673 238.481091 241.245598 244.277939 247.389927
86 87 88 89 90
250.870087 254.263278 257.290902 260.284917 263.297285
91 92 93 94 95
266.446982 269.533920 272.604734 275.381499 277.856506
96 97 98 99 100
279.911123 281.971396 284.602967 287.485559 290.680260
101 102 103 104 105
293.551523 296.266711 299.605007 303.171124 306.864141
106 107 108 109 110
310.739882 314.525911 318.360181 322.538665 326.322461
111 112 113 114 115
330.118534 333.849196 337.655678 341.526317 345.187727
116 117 118 119 120
349.309382 353.508311 358.296185 363.367933 368.966584
121 122 123 124 125
374.267862 377.632636 380.099120 384.175885 389.738167
126 127 128 129 130
395.416022 400.135907 403.181515 405.529043 407.658710
131 132 133 134 135
410.178151 414.445117 420.062777 424.998437 427.634785
136 137 138 139 140
429.856978 431.706527 433.447346 435.334460 437.400839
141 142 143 144 145
439.752200 442.014354 444.627155 446.693187 448.789884
146 147 148 149 150
450.512854 451.884791 453.426109 455.041054 456.546206
151 152 153 154 155
459.032642 462.838856 467.866117 472.342749 477.224466
156 157 158 159 160
480.996570 482.000821 482.567512 482.737502 484.203695
161 162 163 164 165
485.940018 489.158471 492.530256 496.630540 502.383112
166 167 168 169 170
509.451599 517.630030 526.799117 534.952202 542.484574
171 172 173 174 175
550.116811 557.572100 564.700426 573.772520 585.341235
176 177 178 179 180
598.236792 612.550376 627.003636 640.699238 655.788548
181 182
670.291377 683.192409 cumsum(tapply(all_data$all_pre_data$RECO_predict_control_q025,
round(all_data$all_pre_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
-3.98324084 -8.53221103 -13.11177581 -16.56801958
5 6 7 8
-19.24103997 -21.15085443 -22.09948929 -21.93134766
9 10 11 12
-22.63654160 -23.95107528 -24.71130687 -24.61261006
13 14 15 16
-24.56544909 -23.08207089 -21.26533854 -19.58575473
17 18 19 20
-18.72101253 -18.79911390 -18.65428873 -19.06422514
21 22 23 24
-21.40951650 -24.42515147 -27.35688607 -30.06316853
25 26 27 28
-32.65299172 -34.99485606 -33.95759033 -33.08781840
29 30 31 32
-32.41492317 -31.74777007 -31.01664290 -28.31226207
33 34 35 36
-24.95282877 -22.28852254 -19.52480033 -17.19353804
37 38 39 40
-14.94004971 -12.39730116 -9.59648657 -6.62698194
41 42 43 44
-3.73527322 -1.27213282 0.51986316 2.39018120
45 46 47 48
4.08016942 5.06748302 5.22316249 5.20703997
49 50 51 52
5.84139162 6.75767681 6.66185967 6.57412463
53 54 55 56
6.95685899 6.76944121 7.09211471 7.84770632
57 58 59 60
7.53456422 6.44945614 6.48689720 6.36928739
61 62 63 64
6.47835601 7.08886077 7.60021729 8.38297088
65 66 67 68
9.43214447 10.55294236 11.55189776 11.83138853
69 70 71 72
12.98684202 14.51914632 15.20292188 16.44940410
73 74 75 76
17.74243888 19.10926025 19.44757888 20.29303727
77 78 79 80
20.32104736 18.58533844 19.34597483 20.45258349
81 82 83 84
21.50885219 21.38119238 21.11568198 21.30506307
85 86 87 88
19.97582887 17.04195552 10.70747048 6.68819087
89 90 91 92
3.74987562 1.69325490 -0.44198127 -4.69553701
93 94 95 96
-9.15299400 -12.92272092 -13.40169795 -13.53006559
97 98 99 100
-13.94859251 -13.01814756 -11.87971694 -10.16814247
101 102 103 104
-8.32626481 -6.93601966 -5.20973804 -3.45555749
105 106 107 108
-1.66907386 0.47266359 2.57403821 4.78260104
109 110 111 112
7.41523170 9.87144516 11.80819673 13.98525071
113 114 115 116
16.06312994 18.51246166 20.51352899 22.71720539
117 118 119 120
24.96088649 27.43723039 29.86736005 32.35820641
121 122 123 124
34.75900657 36.58341568 37.02327373 38.94918908
125 126 127 128
41.04885078 43.00857488 44.10218257 43.78095758
129 130 131 132
42.60979211 41.07938612 39.92264646 39.95191611
133 134 135 136
40.77322839 41.26275448 40.18707971 38.73643413
137 138 139 140
35.51441647 32.61873525 30.12663087 27.47236173
141 142 143 144
24.93000104 21.94184719 19.28260681 15.78315340
145 146 147 148
12.62273769 8.98182616 4.37758843 -0.01471713
149 150 151 152
-4.18473907 -8.70066761 -12.45842032 -15.11703509
153 154 155 156
-16.33175159 -18.28417370 -19.61802769 -23.01899705
157 158 159 160
-29.92124404 -37.36572445 -46.01640655 -52.66743424
161 162 163 164
-59.10786247 -63.38701246 -67.55862963 -70.68766144
165 166 167 168
-71.61199308 -70.31629943 -67.43732777 -62.14653025
169 170 171 172
-57.05790594 -52.69248921 -48.21763098 -43.17297483
173 174 175 176
-39.51291156 -34.08150537 -27.57690913 -21.26464666
177 178 179 180
-14.95408241 -8.73762489 -2.93443186 2.49499373
181 182
7.57496132 11.97916886 cumsum(tapply(all_data$all_pre_data$RECO_predict_control_q975 ,
round(all_data$all_pre_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4 5
6.035452 11.581554 16.761808 22.232748 27.728455
6 7 8 9 10
33.237893 39.169743 45.468913 51.148259 56.680550
11 12 13 14 15
62.647002 68.909933 75.108078 82.456706 91.541691
16 17 18 19 20
100.376133 108.638250 116.253907 124.039650 131.658607
21 22 23 24 25
138.033620 144.000963 149.869677 155.784305 161.972937
26 27 28 29 30
166.736731 171.222647 174.803624 178.286421 182.233646
31 32 33 34 35
188.106903 194.253906 199.958921 206.161352 211.864250
36 37 38 39 40
217.776653 223.721030 230.271521 236.790183 243.712145
41 42 43 44 45
250.655746 258.439889 266.940203 273.816786 279.240160
46 47 48 49 50
283.166567 287.013052 290.270090 294.840879 299.411522
51 52 53 54 55
303.329177 306.875963 310.167358 313.236888 315.776245
56 57 58 59 60
318.782284 322.055625 325.734943 329.574222 333.201684
61 62 63 64 65
336.860524 341.138657 345.655323 350.372531 355.192796
66 67 68 69 70
361.539417 370.263404 379.388147 388.076518 395.057689
71 72 73 74 75
401.773797 409.119861 418.221190 427.347319 433.170939
76 77 78 79 80
441.222198 447.824268 452.783615 456.501769 460.526931
81 82 83 84 85
465.410430 469.294381 474.241298 479.504507 485.546269
86 87 88 89 90
493.089142 500.829087 507.201210 513.402030 519.125182
91 92 93 94 95
525.177613 531.329048 537.371980 542.582536 547.276368
96 97 98 99 100
551.266377 555.385201 559.507155 563.973013 568.657535
101 102 103 104 105
572.703030 576.994137 582.275701 587.649199 593.321849
106 107 108 109 110
599.196684 604.914405 610.460170 616.713103 622.110322
111 112 113 114 115
628.374309 634.256235 640.488399 646.227829 651.993739
116 117 118 119 120
658.546484 665.230238 672.789683 680.787906 689.561745
121 122 123 124 125
697.456739 702.286452 706.793688 713.053196 722.041634
126 127 128 129 130
731.197402 739.402840 745.876736 751.895498 757.818875
131 132 133 134 135
764.260101 773.177652 784.322928 794.555452 801.747531
136 137 138 139 140
808.908326 817.815819 826.824601 836.457672 846.956932
141 142 143 144 145
857.687991 868.248765 879.205705 888.717938 898.258074
146 147 148 149 150
907.798286 917.747534 927.909818 938.053556 948.277637
151 152 153 154 155
959.103011 971.318247 984.210922 996.513510 1008.814236
156 157 158 159 160
1020.470221 1029.924058 1039.884379 1050.134217 1060.506182
161 162 163 164 165
1070.811146 1082.514032 1094.176872 1106.710440 1120.568246
166 167 168 169 170
1134.973784 1149.715414 1163.796496 1175.895005 1187.250578
171 172 173 174 175
1198.548294 1208.498739 1219.571193 1232.672796 1249.672515
176 177 178 179 180
1269.562896 1292.360814 1315.434721 1337.223769 1361.999538
181 182
1385.608710 1406.296062 #Cumlative sum GPP control side pre compost application with CI’s
cumsum(tapply(all_data$all_pre_data$GPP_control_mean ,
round(all_data$all_pre_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
-0.6068978 -0.9810985 -0.9691959 -1.1333696
5 6 7 8
-1.3839861 -2.0808702 -3.3685294 -4.8280678
9 10 11 12
-6.2530937 -7.1906373 -8.1935658 -9.7100122
13 14 15 16
-10.8643933 -13.1641878 -16.3272762 -18.8921621
17 18 19 20
-21.0255466 -22.7897893 -24.9622262 -26.6663005
21 22 23 24
-29.0154298 -31.4396397 -33.9997613 -36.4348900
25 26 27 28
-38.9712794 -40.9530989 -41.0140883 -40.9870994
29 30 31 32
-40.8941683 -40.3724127 -40.4934864 -40.5417685
33 34 35 36
-40.4781428 -38.8337791 -37.7153261 -36.7529319
37 38 39 40
-36.0502987 -36.1693548 -36.9392567 -37.9731384
41 42 43 44
-38.9802492 -39.8816805 -40.8913955 -42.5448520
45 46 47 48
-43.9455858 -44.8335938 -45.7821701 -46.1864936
49 50 51 52
-47.2002886 -48.0563188 -48.2874585 -49.2095615
53 54 55 56
-50.2572265 -51.4531126 -52.8700482 -54.2607800
57 58 59 60
-55.6921038 -56.9639940 -58.8799935 -60.6273020
61 62 63 64
-62.1192548 -64.1888554 -66.4060911 -68.8777677
65 66 67 68
-71.0441508 -72.8627186 -74.6997126 -77.6643488
69 70 71 72
-80.0135566 -82.5284880 -84.6177928 -86.9998667
73 74 75 76
-89.4827907 -92.5239125 -94.6214105 -96.8105687
77 78 79 80
-98.4757626 -101.1942178 -103.6238707 -105.6641362
81 82 83 84
-107.8832822 -109.2865674 -111.9072731 -114.4911228
85 86 87 88
-117.2910071 -120.8640065 -123.8900472 -126.7917120
89 90 91 92
-130.3006525 -133.8825212 -137.9076650 -141.6836389
93 94 95 96
-145.7101585 -149.2979969 -152.1603298 -154.5888944
97 98 99 100
-157.2892468 -160.6379054 -164.3551646 -167.9928056
101 102 103 104
-171.2650433 -174.1191368 -177.0775121 -180.8110359
105 106 107 108
-186.3196404 -190.9795592 -195.7684236 -200.3673776
109 110 111 112
-204.5583167 -208.8157100 -212.8771848 -216.9467267
113 114 115 116
-220.6142341 -224.9666655 -229.4475133 -233.5535638
117 118 119 120
-237.1081591 -241.5258620 -245.4779294 -249.7061100
121 122 123 124
-254.6132475 -258.1313758 -260.8554404 -264.4238498
125 126 127 128
-268.5934979 -274.0593668 -279.1298404 -283.5637353
129 130 131 132
-287.1904573 -290.5901673 -293.3358885 -297.8119551
133 134 135 136
-302.4584323 -307.7156476 -312.5067610 -314.9959924
137 138 139 140
-317.6882342 -321.5755831 -326.2589319 -331.7708997
141 142 143 144
-337.8102170 -343.2791967 -348.7994589 -354.9415330
145 146 147 148
-360.5466851 -366.2871518 -371.6062326 -376.9154164
149 150 151 152
-381.3162711 -385.1356318 -389.4638263 -394.3926848
153 154 155 156
-399.7544046 -404.4787093 -410.2111943 -415.6421914
157 158 159 160
-419.1806664 -423.3371056 -426.4197553 -430.1145834
161 162 163 164
-433.3180860 -437.3022429 -442.8975620 -448.1020951
165 166 167 168
-453.3896935 -458.4421979 -464.4237754 -471.7407759
169 170 171 172
-478.7445384 -484.8457536 -491.2902667 -497.1036534
173 174 175 176
-502.7736193 -508.6290032 -516.2001259 -524.5242755
177 178 179 180
-533.3606807 -542.2220001 -550.9911210 -561.4832389
181 182
-571.8835723 -581.6260497 cumsum(tapply(all_data$all_pre_data$GPP_control_q025,
round(all_data$all_pre_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
0.94558711 2.63050177 4.65862854 6.27934651
5 6 7 8
7.56561054 8.32625087 8.45157555 8.05326581
9 10 11 12
8.14538127 8.48660769 9.18716656 9.13469319
13 14 15 16
9.20544826 7.59413984 5.52227906 3.83179171
17 18 19 20
3.08307847 2.73553707 1.79203665 1.31035308
21 22 23 24
0.03358712 -1.86369013 -4.29156100 -6.77603035
25 26 27 28
-9.65392069 -12.32838697 -12.81719800 -12.63683294
29 30 31 32
-12.51844501 -12.11341069 -12.10388384 -12.32820863
33 34 35 36
-12.58385435 -11.35534988 -10.10806233 -9.60718624
37 38 39 40
-9.19395536 -9.30726878 -10.28423810 -10.98605726
41 42 43 44
-11.95023164 -12.44015996 -12.69577587 -13.79865988
45 46 47 48
-14.91056954 -15.52213037 -15.94191873 -15.98780723
49 50 51 52
-16.75864913 -17.66566071 -17.51034863 -18.02413650
53 54 55 56
-18.77884297 -19.45620572 -20.83936610 -21.79322214
57 58 59 60
-22.57868853 -22.85863332 -24.61930251 -25.81716732
61 62 63 64
-26.60911463 -28.40338150 -30.19716436 -32.39231421
65 66 67 68
-34.22129188 -35.75030241 -37.28522070 -39.65453764
69 70 71 72
-41.88513120 -44.48969775 -45.88176921 -47.90037320
73 74 75 76
-50.06782695 -52.84163366 -54.75394086 -56.58720882
77 78 79 80
-58.02964943 -59.81621386 -62.04326415 -63.73753227
81 82 83 84
-65.63584398 -66.36349366 -67.95345424 -69.86911527
85 86 87 88
-71.49576996 -73.03771291 -72.80243572 -73.34957593
89 90 91 92
-75.01426000 -77.09233752 -79.63103245 -81.12235781
93 94 95 96
-82.65675775 -83.84153429 -86.07702251 -87.93011657
97 98 99 100
-89.42849942 -92.27891725 -95.42893430 -99.06106496
101 102 103 104
-102.34414273 -104.78039367 -107.29539875 -110.79303896
105 106 107 108
-115.98067760 -120.64052818 -125.29147633 -129.68219782
109 110 111 112
-133.56149833 -137.34708600 -141.31073868 -145.16471667
113 114 115 116
-148.59336619 -152.85171608 -157.07334467 -160.84660878
117 118 119 120
-163.82405270 -167.41845720 -170.88646853 -174.39100611
121 122 123 124
-178.53328378 -181.74262753 -183.79692313 -186.33284176
125 126 127 128
-189.77582043 -194.57486987 -199.14513060 -203.28247219
129 130 131 132
-206.56821515 -209.61018585 -211.91779209 -215.80423687
133 134 135 136
-219.65405906 -224.23088901 -228.79854747 -230.84014350
137 138 139 140
-232.64457044 -235.86446329 -240.03112753 -245.20842776
141 142 143 144
-250.96412217 -256.14733803 -261.58399253 -267.86914374
145 146 147 148
-274.16702743 -280.57581951 -286.21510192 -291.10630863
149 150 151 152
-294.59569392 -296.93416451 -299.45034066 -302.13794198
153 154 155 156
-305.00499258 -306.95555106 -309.96683363 -312.69723752
157 158 159 160
-313.30456482 -314.02095993 -313.54931064 -314.16003578
161 162 163 164
-314.42504764 -315.61913487 -318.67232287 -321.87009754
165 166 167 168
-325.63742955 -329.54193779 -334.69054266 -342.34672983
169 170 171 172
-349.85709728 -355.88419690 -362.01809874 -367.56230006
173 174 175 176
-372.16254263 -376.72233620 -382.26073194 -388.32209662
177 178 179 180
-393.97218202 -399.22738506 -404.68381769 -410.97591382
181 182
-417.56755149 -424.26251410 cumsum(tapply(all_data$all_pre_data$GPP_control_q975 ,
round(all_data$all_pre_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4 5
-2.415176 -4.684920 -6.713123 -8.656301 -10.554526
6 7 8 9 10
-12.672176 -15.066591 -17.789613 -20.089185 -22.487769
11 12 13 14 15
-24.773466 -27.151680 -29.279539 -32.198364 -36.184983
16 17 18 19 20
-39.847949 -42.926722 -45.805863 -48.792839 -51.316592
21 22 23 24 25
-54.436803 -57.378502 -60.359450 -63.131737 -65.956356
26 27 28 29 30
-67.909098 -67.731525 -67.610046 -67.545999 -67.000293
31 32 33 34 35
-67.501597 -67.816999 -68.185809 -66.015769 -64.200341
36 37 38 39 40
-63.106259 -62.317527 -62.392728 -63.454835 -64.741897
41 42 43 44 45
-66.361967 -67.975439 -69.841344 -72.118492 -73.822739
46 47 48 49 50
-75.001764 -76.148035 -76.673444 -77.771392 -79.047096
51 52 53 54 55
-79.285620 -80.374369 -81.542836 -83.031867 -84.520710
56 57 58 59 60
-86.113971 -87.891788 -89.760104 -91.968723 -94.071857
61 62 63 64 65
-95.816224 -98.191107 -100.541928 -103.210452 -105.696558
66 67 68 69 70
-108.357034 -112.031714 -116.602484 -120.814616 -123.823434
71 72 73 74 75
-126.888637 -130.278587 -134.706067 -139.640500 -142.253228
76 77 78 79 80
-145.849522 -148.209600 -151.699402 -154.636507 -156.813701
81 82 83 84 85
-159.387502 -161.066072 -164.064907 -167.210179 -170.580109
86 87 88 89 90
-175.116777 -179.021464 -182.581920 -186.776403 -190.677128
91 92 93 94 95
-195.058366 -199.453152 -203.879054 -207.658802 -210.708788
96 97 98 99 100
-214.186038 -217.349169 -220.987204 -224.613171 -228.562198
101 102 103 104 105
-232.144165 -235.579225 -239.216001 -243.298956 -248.727439
106 107 108 109 110
-253.900421 -258.995871 -263.873501 -268.670983 -273.185716
111 112 113 114 115
-277.993780 -282.609892 -287.007917 -291.673798 -296.647549
116 117 118 119 120
-301.619718 -306.023388 -310.922687 -316.058823 -321.417108
121 122 123 124 125
-326.907426 -330.413305 -334.090763 -338.231377 -343.655749
126 127 128 129 130
-349.855765 -355.364438 -360.132906 -364.110879 -368.036032
131 132 133 134 135
-371.389029 -376.798792 -382.719889 -389.031352 -394.754083
136 137 138 139 140
-398.262026 -402.806448 -408.993048 -416.189542 -424.335748
141 142 143 144 145
-432.831012 -440.476397 -448.122753 -455.946171 -463.199857
146 147 148 149 150
-471.226149 -479.182129 -487.244223 -494.083610 -500.515224
151 152 153 154 155
-507.330480 -515.214463 -523.633490 -531.670577 -540.444561
156 157 158 159 160
-548.818838 -555.543957 -563.316082 -570.457768 -577.662803
161 162 163 164 165
-584.032036 -591.150004 -599.353190 -606.929299 -614.364080
166 167 168 169 170
-621.285244 -628.827711 -636.750550 -644.281450 -651.457539
171 172 173 174 175
-659.182780 -665.771101 -673.044556 -680.615896 -690.362301
176 177 178 179 180
-701.010812 -713.194245 -725.904678 -738.234433 -752.588975
181 182
-766.539823 -778.970721 #Cumlative sum Reco control side post compost application with CI’s
#Reco Cumsum
cumsum(tapply(all_data$all_post_data$RECO_predict_control_mean,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4 5
0.6836607 1.5385510 2.5120066 3.4281574 4.8052893
6 7 8 9 10
6.3771841 7.0750782 7.6770860 9.0379044 10.3841561
11 12 13 14 15
11.6884741 13.1400178 14.7330553 16.2908555 17.9068565
16 17 18 19 20
19.6506667 21.4116179 23.1909294 24.9386742 26.7025539
21 22 23 24 25
28.3660081 29.7671302 31.4181495 32.9631360 34.6121927
26 27 28 29 30
35.9427433 37.2587164 38.4288327 39.3199165 40.2071456
31 32 33 34 35
41.2433594 42.1732404 42.9830760 43.7845742 44.5246586
36 37 38 39 40
45.2410848 46.1961715 47.3857948 48.3801868 49.5093677
41 42 43 44 45
50.5953342 51.8994561 53.2073196 54.3892645 55.4779478
46 47 48 49 50
56.7792727 58.8221343 60.5310318 62.1797241 63.7974026
51 52 53 54 55
65.4166379 67.2550348 69.0349187 70.6201354 72.5763637
56 57 58 59 60
74.8772111 76.9485650 78.9988737 81.2802987 83.7434461
61 62 63 64 65
86.6491688 89.4373368 92.2569655 95.0395029 98.1359506
66 67 68 69 70
100.9510838 103.2244627 105.7299733 107.9450334 109.8190888
71 72 73 74 75
111.4453362 113.0929159 115.0144316 117.0474033 119.4419604
76 77 78 79 80
121.4580811 123.5242996 125.7497518 128.0361234 130.0391979
81 82 83 84 85
131.8315639 133.2952674 134.5988048 135.8110933 137.0400760
86 87 88 89 90
137.5431718 137.4553702 137.1513808 138.8477938 141.7199560
91 92 93 94 95
144.8199803 148.2537424 151.9158282 155.9315416 159.4763535
96 97 98 99 100
162.7643552 165.9837410 169.1601587 172.1288488 175.1336317
101 102 103 104 105
179.6856422 184.2461628 188.0144232 191.4490574 195.2045716
106 107 108 109 110
197.6830005 200.7028988 202.9280779 204.6531266 206.0980140
111 112 113 114 115
207.9721733 209.6771037 211.4799109 214.1260222 216.8697049
116 117 118 119 120
219.4458529 221.9212768 224.4478156 226.9652286 228.9433034
121 122 123 124 125
230.8812059 232.9919796 235.3237164 237.5585353 240.0381047
126 127 128 129 130
242.6667903 245.2940726 247.9557189 250.7040759 253.0839619
131 132 133 134 135
256.0351456 259.3460674 262.7349770 266.0966795 268.6889144
136 137 138 139 140
271.4801226 274.5296599 277.8443973 281.1831887 284.2600765
141 142 143 144 145
287.4263334 290.5397363 293.2891133 295.8139436 298.4364524
146 147 148 149 150
301.0259544 303.5875080 306.1052336 308.6224593 311.1378701
151 152 153 154 155
313.6814345 316.4045736 319.1889335 321.6747005 324.0997897
156 157 158 159 160
326.5542878 329.2165947 331.7241091 334.5217297 337.1091017
161 162 163 164 165
339.8676992 342.7666727 345.6905727 348.6578380 351.3324252
166 167 168 169 170
354.1878165 356.9351655 359.6049453 362.3148558 365.2022727
171 172 173 174 175
368.3045377 370.7430761 372.7974354 374.7661834 376.5453847
176 177 178 179 180
378.0496140 379.6402144 381.3579480 383.1679345 385.0748371
181
386.7698375 #lower bound CI
cumsum(tapply(all_data$all_post_data$RECO_predict_control_q025,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4 5
-1.4821082 -2.7307099 -3.5604128 -4.2765855 -4.7702397
6 7 8 9 10
-4.6735843 -4.7434472 -5.1475794 -5.2903741 -5.2596883
11 12 13 14 15
-5.2726989 -5.0398897 -4.2798573 -3.6957956 -3.1628107
16 17 18 19 20
-2.4780522 -1.8392650 -1.6294335 -0.9018073 0.2732740
21 22 23 24 25
1.3458613 1.8923070 2.8930250 3.5427426 4.3969389
26 27 28 29 30
4.9999518 5.7595465 5.6822106 5.3967360 5.2542905
31 32 33 34 35
5.4834753 5.4050509 5.2325570 5.0487369 4.7481456
36 37 38 39 40
4.4408825 4.7176594 5.3308876 5.4671453 5.7713000
41 42 43 44 45
5.8540922 6.0998911 6.1791746 5.8018210 5.1192187
46 47 48 49 50
4.6015755 5.1604869 5.3406350 5.4106024 5.3221318
51 52 53 54 55
4.8676276 5.8320794 6.6907898 7.0534286 8.0665353
56 57 58 59 60
9.4469858 10.5489135 11.7177044 12.5436547 12.8048534
61 62 63 64 65
14.3749602 15.7220685 17.1070985 18.5822979 20.4196712
66 67 68 69 70
21.9878325 22.8939789 24.3913985 25.4690957 25.7162941
71 72 73 74 75
25.7646285 25.7955298 26.5373413 27.4989216 29.2876488
76 77 78 79 80
30.3543865 31.6160343 33.2848219 35.0869088 36.5765479
81 82 83 84 85
37.5400826 37.7770245 37.9073692 38.1861707 38.3482589
86 87 88 89 90
37.1338932 33.6225848 25.9209852 1.9493551 -39.7762296
91 92 93 94 95
-65.5072690 -78.4584854 -81.2010751 -83.1672704 -82.0930247
96 97 98 99 100
-80.1132539 -78.3083882 -76.7978222 -76.1176013 -75.9159108
101 102 103 104 105
-73.8610978 -71.4387298 -69.6176658 -68.9101702 -67.6221491
106 107 108 109 110
-69.5729149 -70.3234677 -72.4980289 -75.5838262 -77.7300131
111 112 113 114 115
-79.1374585 -80.2616610 -80.2786106 -78.5200245 -76.9241831
116 117 118 119 120
-75.3162387 -73.5977347 -71.9852750 -70.7765258 -69.9993028
121 122 123 124 125
-69.5034198 -68.9690225 -68.1856259 -67.6066389 -67.0697494
126 127 128 129 130
-66.3930769 -65.7955310 -65.2364948 -64.8545429 -64.8368598
131 132 133 134 135
-65.1969286 -64.6689245 -63.5973509 -62.5096476 -62.3565033
136 137 138 139 140
-62.3372628 -61.8112125 -60.3087073 -58.5671748 -57.1416236
141 142 143 144 145
-55.5888903 -53.7090223 -52.0379028 -50.7007218 -49.5875307
146 147 148 149 150
-48.5501823 -47.4946491 -46.1797560 -44.5656940 -43.0163312
151 152 153 154 155
-41.5681496 -40.3653568 -39.0808737 -37.7230858 -36.4569216
156 157 158 159 160
-35.2188592 -33.9103726 -32.7384663 -31.5172364 -30.4786369
161 162 163 164 165
-29.6201889 -28.8352679 -28.2942169 -27.2490828 -26.5248768
166 167 168 169 170
-25.6712742 -25.0710887 -24.8535949 -25.2620390 -25.6933973
171 172 173 174 175
-24.6978876 -24.0930376 -23.7635026 -23.8978053 -24.0812870
176 177 178 179 180
-24.7603951 -25.8462287 -26.9467330 -28.2722558 -30.0277546
181
-31.9205730 #upper bound CI
cumsum(tapply(all_data$all_post_data$RECO_predict_control_q975 ,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4 5
3.270686 6.573843 9.794791 12.685424 16.374993
6 7 8 9 10
20.067618 21.532268 23.112494 26.169107 28.881433
11 12 13 14 15
31.333352 34.088725 36.494408 38.923490 41.557720
16 17 18 19 20
44.181433 46.779067 49.684798 52.315970 54.698380
21 22 23 24 25
56.967678 59.228771 61.422153 63.737362 66.061942
26 27 28 29 30
68.056191 69.918885 72.520655 74.544142 76.370333
31 32 33 34 35
78.188466 80.187986 81.908205 83.601628 85.255653
36 37 38 39 40
86.855295 88.444698 90.238484 92.124685 94.124435
41 42 43 44 45
96.328623 99.027064 102.168120 105.520506 109.025426
46 47 48 49 50
112.811932 116.646830 119.916906 123.386434 127.006207
51 52 53 54 55
131.395277 134.228423 136.940823 139.743565 142.697234
56 57 58 59 60
145.933031 148.918685 151.837005 156.161082 161.552226
61 62 63 64 65
167.118521 172.725284 178.155731 182.738208 187.622623
66 67 68 69 70
192.419589 196.224781 199.884510 203.128139 206.368474
71 72 73 74 75
209.348829 212.347101 215.313480 218.241070 221.239923
76 77 78 79 80
224.065444 226.793416 229.511634 232.255188 234.745365
81 82 83 84 85
237.285875 239.778934 242.055071 244.361011 247.080115
86 87 88 89 90
249.009274 252.084780 254.917469 269.863133 297.773631
91 92 93 94 95
316.830876 329.543814 337.610651 345.996303 352.064944
96 97 98 99 100
356.778794 361.548050 366.355781 371.259501 376.831132
101 102 103 104 105
386.130198 394.041498 400.526351 407.067012 414.162892
106 107 108 109 110
420.070435 426.525389 431.773490 436.575701 440.724133
111 112 113 114 115
444.700880 448.183641 451.347778 454.899778 458.649717
116 117 118 119 120
462.095034 465.486887 469.035504 474.127882 477.288568
121 122 123 124 125
480.487766 483.876533 487.813726 491.963180 496.930402
126 127 128 129 130
502.408810 508.350334 514.583044 521.576948 528.012542
131 132 133 134 135
535.601479 543.618292 551.425512 559.446148 566.007965
136 137 138 139 140
573.102526 580.083984 586.659861 592.886473 598.663414
141 142 143 144 145
604.439375 609.503826 613.406666 616.920027 620.588902
146 147 148 149 150
624.430597 628.451762 632.104923 635.536813 638.977599
151 152 153 154 155
642.533235 646.751053 651.338738 655.229982 658.986415
156 157 158 159 160
662.924076 667.433273 671.866848 676.822352 681.604201
161 162 163 164 165
686.675749 692.266499 697.996122 703.310657 708.424488
166 167 168 169 170
713.725411 719.042749 724.497585 730.311170 736.380351
171 172 173 174 175
741.963209 747.236031 752.320212 757.741679 763.017926
176 177 178 179 180
768.418430 774.076475 779.686264 785.268961 790.771413
181
795.459037 #Cumulative sum GPP control side post compost application with CI’s
#GPP Cumsum
cumsum(tapply(all_data$all_post_data$GPP_control_mean,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
0.5727985 1.2580111 1.5534492 1.8519151
5 6 7 8
2.4489729 1.7882448 1.3415414 0.6009846
9 10 11 12
-0.6332844 -1.4497152 -1.9699734 -2.8535612
13 14 15 16
-3.2009131 -3.7972899 -4.8546682 -5.8417725
17 18 19 20
-5.8554100 -5.8584201 -6.1131957 -6.3150359
21 22 23 24
-6.8361602 -7.3019664 -7.9380776 -8.3413320
25 26 27 28
-9.1988337 -9.8641102 -10.6136242 -11.2995091
29 30 31 32
-11.6251665 -12.3098995 -12.8282593 -13.3815193
33 34 35 36
-13.8647499 -14.0968815 -14.5196470 -15.0072550
37 38 39 40
-15.2916004 -15.7314820 -16.2682990 -16.6218118
41 42 43 44
-16.8231389 -16.5965795 -16.0871436 -15.8474088
45 46 47 48
-15.6067182 -15.2376428 -14.9206547 -14.7210389
49 50 51 52
-14.9509662 -14.9679808 -15.3059966 -15.6178247
53 54 55 56
-15.9285261 -16.3533304 -16.4713018 -16.7929973
57 58 59 60
-17.2691661 -17.8226117 -18.4313890 -19.0861167
61 62 63 64
-20.1012280 -20.9814547 -21.1396056 -21.5871166
65 66 67 68
-21.8041286 -22.6986507 -23.4577852 -24.2956514
69 70 71 72
-25.1324726 -25.8163144 -26.5914136 -27.3260265
73 74 75 76
-27.7886115 -28.2920318 -29.2630785 -30.4771433
77 78 79 80
-31.3562657 -32.0113915 -32.8355570 -34.1968441
81 82 83 84
-35.0361439 -35.6160231 -36.0434900 -36.4044427
85 86 87 88
-36.3596624 -36.5646847 -36.3993499 -39.6858454
89 90 91 92
-39.5018672 -40.6131422 -41.9938500 -43.3387127
93 94 95 96
-44.5733205 -46.2898102 -48.0465973 -49.7969081
97 98 99 100
-51.5249195 -53.1005696 -54.5994581 -55.8681030
101 102 103 104
-58.0576827 -60.2716668 -61.7658259 -63.8833217
105 106 107 108
-66.0702411 -67.5344054 -68.9520738 -70.5143407
109 110 111 112
-72.2306897 -74.1942928 -75.5516127 -77.2559314
113 114 115 116
-79.3244625 -81.4588307 -83.7640812 -86.1522129
117 118 119 120
-88.6043709 -91.4611418 -94.4238981 -96.8501744
121 122 123 124
-99.5641713 -102.1349227 -104.9607511 -108.2484214
125 126 127 128
-111.3185021 -114.3107563 -117.4928450 -120.6920807
129 130 131 132
-123.8570347 -126.6286985 -129.5997448 -133.0044985
133 134 135 136
-136.9614294 -140.3943221 -144.2316012 -148.6382790
137 138 139 140
-152.3894658 -155.7895313 -159.5967400 -164.2278146
141 142 143 144
-168.8348376 -173.8201969 -178.3335019 -182.1083623
145 146 147 148
-186.4211204 -190.3898580 -193.8715630 -196.7846800
149 150 151 152
-200.2427209 -203.3167897 -206.5032639 -209.4749243
153 154 155 156
-213.2061521 -217.8553778 -222.1428714 -226.8142125
157 158 159 160
-231.3967711 -235.9940570 -239.5178652 -243.3546377
161 162 163 164
-246.6523785 -249.7118000 -252.8290228 -256.2726342
165 166 167 168
-259.6865973 -263.3674285 -266.9623654 -270.3037202
169 170 171 172
-273.0111845 -276.5234737 -280.5619705 -283.9763748
173 174 175 176
-287.5993282 -291.0585811 -293.9048153 -296.1567475
177 178 179 180
-299.3149336 -302.3854103 -304.9171797 -307.3608331
181
-310.2486675 #comparison of GPP cum sum using reco predict
#making the comparison variable
all_data$all_post_data$GPP_control_with_pred_Reco <- (all_data$all_post_data$NEE_filled_control_mean - all_data$all_post_data$RECO_predict_control_mean)
#cumsum the comparison variable
cumsum(tapply(all_data$all_post_data$GPP_control_with_pred_Reco,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
0.5727985 1.2580111 1.5534492 1.8519151
5 6 7 8
2.4489729 1.7882448 1.3613392 0.5681962
9 10 11 12
-0.6660728 -1.4825036 -2.0027618 -2.8863496
13 14 15 16
-3.2337016 -3.8002977 -4.8576761 -5.9051207
17 18 19 20
-5.9187582 -5.9217683 -6.1765439 -6.3271283
21 22 23 24
-6.7784098 -7.2277412 -7.8638524 -8.2671069
25 26 27 28
-9.1246085 -9.8455007 -10.6611908 -11.3470757
29 30 31 32
-11.6727330 -12.3574661 -12.7769834 -13.3302434
33 34 35 36
-13.8134740 -14.0456056 -14.4683711 -14.9559791
37 38 39 40
-15.3372275 -15.7869848 -16.3496170 -16.7031298
41 42 43 44
-16.9472525 -16.7339651 -16.1140547 -15.8743199
45 46 47 48
-15.6336293 -15.2588758 -14.9418878 -14.7540607
49 50 51 52
-14.9839880 -15.0225633 -15.3605792 -15.5914080
53 54 55 56
-15.9326802 -16.3625377 -16.4936085 -16.8153040
57 58 59 60
-17.3826232 -17.8897387 -18.4985161 -19.1532438
61 62 63 64
-20.1867562 -21.0855826 -21.0838268 -21.5774489
65 66 67 68
-21.7753032 -22.7447454 -23.4710011 -24.2283198
69 70 71 72
-25.0712603 -25.7654223 -26.6293839 -27.3639968
73 74 75 76
-27.8265818 -28.3084669 -29.1818497 -30.4594124
77 78 79 80
-31.3898963 -32.0336814 -32.8119584 -34.2134620
81 82 83 84
-35.0898841 -35.6697634 -36.0972303 -36.4321113
85 86 87 88
-36.3803980 -36.5744387 -36.4091039 -39.6752343
89 90 91 92
-39.4912561 -40.6025311 -41.9832389 -43.3281016
93 94 95 96
-44.5627094 -46.2791991 -48.0359862 -49.9201933
97 98 99 100
-51.5218360 -53.1609713 -54.6598598 -55.7743256
101 102 103 104
-58.0792304 -60.2231968 -61.7682115 -63.8857074
105 106 107 108
-66.0726268 -67.5324679 -68.9501363 -70.4989496
109 110 111 112
-72.2152986 -74.2354239 -75.5927438 -77.2970626
113 114 115 116
-79.3758410 -81.4587060 -83.7955091 -86.1577382
117 118 119 120
-88.6222883 -91.4790592 -94.4418155 -96.9358745
121 122 123 124
-99.6498714 -102.1712002 -104.9970286 -108.2846989
125 126 127 128
-111.3547796 -114.3470338 -117.5291225 -120.7283582
129 130 131 132
-123.8933122 -126.6649760 -129.6360223 -133.0407760
133 134 135 136
-136.9977069 -140.4305996 -144.2678787 -148.7764465
137 138 139 140
-152.3883193 -155.7674837 -159.6173076 -164.2781365
141 142 143 144
-168.8851595 -173.8705188 -178.2872393 -182.1572014
145 146 147 148
-186.4699594 -190.4386971 -193.9204020 -196.8335190
149 150 151 152
-200.2893820 -203.3354432 -206.5366291 -209.5082894
153 154 155 156
-213.2395172 -217.8887430 -222.1762365 -226.8475776
157 158 159 160
-231.4301362 -236.0274221 -239.5512303 -243.3880029
161 162 163 164
-246.6857436 -249.7451652 -252.8623879 -256.3059993
165 166 167 168
-259.7314681 -263.4122993 -267.0072362 -270.3645312
169 170 171 172
-273.0719955 -276.5638508 -280.6023476 -284.0389300
173 174 175 176
-287.6618834 -291.1211362 -293.9673705 -296.2193027
177 178 179 180
-299.3774888 -302.4479655 -304.9797349 -307.4233883
181
-310.3060552 ######################################################################
#lower bound CI
cumsum(tapply(all_data$all_post_data$GPP_control_q025,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
0.7552959 1.7446564 2.2628244 2.7705548
5 6 7 8
3.7480068 3.2785700 2.8842880 2.4262724
9 10 11 12
1.5423788 0.9599527 0.6019023 -0.2305185
13 14 15 16
-0.6293075 -1.3700649 -2.0957159 -2.8093003
17 18 19 20
-2.7158830 -2.3976906 -2.9384869 -3.2970654
21 22 23 24
-3.5851500 -3.9466381 -4.4554535 -4.8591940
25 26 27 28
-5.6096073 -6.1471120 -6.7702827 -7.3118635
29 30 31 32
-7.6534355 -8.0240002 -8.3763741 -8.7633500
33 34 35 36
-9.2256525 -9.3252866 -9.7021401 -10.1371846
37 38 39 40
-10.1072676 -10.3241880 -10.9407142 -11.2494025
41 42 43 44
-11.4751361 -11.4285729 -10.9360698 -10.7542563
45 46 47 48
-10.0122498 -9.4114805 -8.8106654 -8.5057373
49 50 51 52
-8.5248174 -7.9270534 -7.8541419 -8.0199462
53 54 55 56
-8.1750123 -8.1272080 -8.0093860 -8.1597468
57 58 59 60
-8.5842080 -8.9284500 -9.2706124 -9.1840048
61 62 63 64
-9.8639940 -10.1544821 -10.0554773 -10.3721035
65 66 67 68
-10.4759779 -11.2755023 -11.4289401 -12.2148713
69 70 71 72
-12.7480710 -12.8793962 -13.1244778 -13.4210881
73 74 75 76
-13.7641998 -14.1130843 -15.0050220 -16.0518122
77 78 79 80
-16.7021171 -17.3819996 -18.0120951 -19.2822596
81 82 83 84
-19.8204580 -20.3641112 -20.5706217 -20.7540997
85 86 87 88
-20.5164542 -20.0919067 -18.8188383 -21.0966942
89 90 91 92
-9.9708130 8.2189896 18.6198226 23.4792752
93 94 95 96
23.9502529 25.1249249 24.0063621 22.3819757
97 98 99 100
21.2211232 20.2593322 19.4747824 18.8577923
101 102 103 104
17.1980611 15.1525453 14.2273766 12.9774365
105 106 107 108
11.5257268 11.6724312 11.3825158 11.5313678
109 110 111 112
11.5043980 10.5708940 10.4174635 9.7042513
113 114 115 116
8.1933353 6.1560764 4.3093345 1.9867874
117 118 119 120
-0.3905314 -3.0229771 -5.7578479 -8.1344714
121 122 123 124
-10.6587631 -12.9154208 -15.2608278 -18.0313572
125 126 127 128
-20.6030747 -23.4303145 -26.0547979 -28.7258101
129 130 131 132
-31.3570211 -33.4516458 -35.4149442 -37.5707871
133 134 135 136
-40.7932884 -43.5770287 -46.6591876 -50.1265723
137 138 139 140
-53.2407449 -56.1351240 -59.6709258 -63.8507430
141 142 143 144
-68.0855546 -72.5609993 -76.9293264 -80.6330871
145 146 147 148
-84.7057669 -88.2853550 -91.1445418 -93.8402529
149 150 151 152
-97.1099411 -99.8958021 -102.5517260 -105.1221885
153 154 155 156
-108.4495550 -113.1451537 -117.5404658 -122.1684149
157 158 159 160
-126.6642588 -131.0712691 -134.2664023 -137.7050460
161 162 163 164
-140.3911735 -142.7719024 -144.8088528 -147.6430227
165 166 167 168
-150.3345603 -153.2500693 -156.1938732 -158.7388973
169 170 171 172
-160.2580114 -162.8341869 -166.4669102 -169.5185043
173 174 175 176
-172.7445022 -175.6540445 -177.9391648 -179.4436734
177 178 179 180
-181.6157548 -183.4134348 -184.3947336 -185.2335976
181
-186.8030329 ######################################################################
#upper bound CI
cumsum(tapply(all_data$all_post_data$GPP_control_q975 ,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
0.46207475 0.84456215 0.85562499 0.87189482
5 6 7 8
1.01702245 -0.05270165 -0.66351211 -1.57696578
9 10 11 12
-3.20276042 -4.32354175 -4.91972145 -5.85115641
13 14 15 16
-6.11258949 -6.96440642 -7.96284868 -8.93546487
17 18 19 20
-8.96705998 -9.10436628 -9.61974709 -10.01223870
21 22 23 24
-10.42451146 -11.03988839 -11.57558131 -12.08003535
25 26 27 28
-12.88946662 -13.52924961 -14.25503691 -15.13273816
29 30 31 32
-15.89463917 -16.66183724 -17.22918555 -17.94834309
33 34 35 36
-18.57512582 -18.81061267 -19.41290517 -20.04909768
37 38 39 40
-20.28907174 -20.53286248 -21.18558168 -21.46742982
41 42 43 44
-21.72984791 -21.80839849 -21.55706673 -21.94282153
45 46 47 48
-21.96154954 -22.09469340 -22.07532758 -22.17810942
49 50 51 52
-22.74684565 -23.16743804 -24.36613834 -24.86738736
53 54 55 56
-25.52228058 -26.16535596 -26.33272349 -26.69934105
57 58 59 60
-27.49469530 -28.09278731 -29.44352645 -31.11728616
61 62 63 64
-32.92445076 -34.65587190 -35.53095913 -36.36079897
65 66 67 68
-36.84549344 -38.44589051 -39.51492661 -40.78238106
69 70 71 72
-41.66427733 -42.75258319 -43.84223551 -45.00449420
73 74 75 76
-45.64914637 -46.28067394 -47.24890422 -48.50991714
77 78 79 80
-49.41760287 -50.26864457 -51.03413431 -52.35882835
81 82 83 84
-53.27337987 -54.17517315 -54.68498093 -55.23184365
85 86 87 88
-55.55730785 -56.31104207 -56.92565060 -59.78081417
89 90 91 92
-64.83440936 -76.74947065 -84.57755297 -89.37488079
93 94 95 96
-91.65094410 -95.27229084 -97.65228546 -99.95301617
97 98 99 100
-101.94912037 -104.03423930 -106.12953395 -108.08826174
101 102 103 104
-111.31172059 -114.65148679 -116.82014110 -120.02152945
105 106 107 108
-123.20520717 -125.74399516 -128.21837856 -130.72397243
109 110 111 112
-133.44992941 -136.09148533 -137.94362098 -140.31071246
113 114 115 116
-142.76031158 -145.07963467 -147.47246182 -150.13099093
117 118 119 120
-152.85447145 -155.75011099 -159.51783056 -162.42360267
121 122 123 124
-165.31567466 -168.02918847 -170.94431275 -174.66585629
125 126 127 128
-178.50601730 -182.85698473 -187.17649192 -191.59952438
129 130 131 132
-196.19892670 -200.29716053 -205.12579070 -210.23585246
133 134 135 136
-215.93523134 -221.23285356 -226.66850161 -232.65128981
137 138 139 140
-237.96984483 -242.35328990 -247.06841584 -252.47971895
141 142 143 144
-258.02047391 -263.41119879 -268.09810521 -272.17572185
145 146 147 148
-276.75947944 -281.11018730 -285.03409364 -288.44666240
149 150 151 152
-292.07334618 -295.49279856 -298.89342384 -302.21032442
153 154 155 156
-306.12288011 -310.82869563 -315.19770382 -319.99536521
157 158 159 160
-325.00548290 -330.08038050 -334.45595311 -339.07583109
161 162 163 164
-343.26952167 -347.57179001 -351.67511616 -356.04350172
165 166 167 168
-360.47706565 -365.21190265 -369.93487945 -374.49107732
169 170 171 172
-378.30571075 -383.01033682 -387.71104965 -392.02010838
173 174 175 176
-396.66296168 -401.32542546 -405.44551621 -409.32375752
177 178 179 180
-414.21651821 -418.99802238 -423.30344934 -427.63363294
181
-431.71282960 #Cumlative sum Reco Treatment side pre compost application with CI’s
cumsum(tapply(all_data$all_pre_data$RECO_predict_treatment_mean,
round(all_data$all_pre_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4 5
0.6820033 1.4324343 2.4127149 3.2144412 4.1289307
6 7 8 9 10
4.9988042 5.7974932 6.6083216 7.4140765 8.3041714
11 12 13 14 15
9.3795812 10.2976389 11.1179050 11.9906994 12.9574582
16 17 18 19 20
13.7938811 14.8406842 15.8018493 17.2149314 18.4971313
21 22 23 24 25
19.7924967 21.2185352 22.8314431 24.5421333 26.2518955
26 27 28 29 30
27.6461405 31.2017359 34.0142744 36.3615765 38.1963412
31 32 33 34 35
42.3079427 47.4164471 52.5630141 58.0704011 63.4610517
36 37 38 39 40
68.8309913 74.3441559 79.5216652 84.0239528 88.4647066
41 42 43 44 45
93.0221467 98.1294080 103.0654634 107.5956864 111.2652406
46 47 48 49 50
114.6004225 117.5921010 120.9912549 124.8728635 128.3300547
51 52 53 54 55
131.7357180 135.2180815 138.3538593 141.2567607 144.1957302
56 57 58 59 60
147.3494339 150.2095920 152.9845380 156.0135182 159.5967355
61 62 63 64 65
163.3070795 166.9472914 170.5280346 173.6955712 177.1559875
66 67 68 69 70
180.2849066 183.1878838 185.7497223 188.7428683 191.6816858
71 72 73 74 75
194.2830465 197.2199338 199.6511790 202.2934332 205.1893560
76 77 78 79 80
207.5205973 209.8522183 211.8952470 214.1081019 216.3084878
81 82 83 84 85
218.5542311 221.7367843 225.3496433 228.6291985 232.2641053
86 87 88 89 90
236.5548068 241.1522210 245.0918732 249.0357998 252.5865081
91 92 93 94 95
256.5690095 260.6265393 264.3271238 268.0422351 270.8870982
96 97 98 99 100
273.0835547 275.5945315 278.5589542 281.7686284 284.9140205
101 102 103 104 105
288.0231357 291.4711137 295.5109178 299.3427102 302.6287302
106 107 108 109 110
306.0020996 309.5819021 313.5229734 317.3633138 321.2548479
111 112 113 114 115
324.6138181 328.0224188 331.6509547 335.5369310 339.1457088
116 117 118 119 120
342.7666762 346.8494244 350.6545082 354.6036892 358.6844513
121 122 123 124 125
362.4165109 365.3526576 368.4608756 372.2829593 376.0072913
126 127 128 129 130
379.3141209 382.3819816 384.7965814 386.7371843 388.9131163
131 132 133 134 135
391.6241804 394.6430359 397.6340214 400.2593611 402.4357542
136 137 138 139 140
404.5910983 406.6722740 408.3127429 409.9056807 411.6432985
141 142 143 144 145
413.5014399 415.2642408 417.1473941 419.1002210 420.8135564
146 147 148 149 150
422.2899566 423.8722125 425.6929525 427.9946128 430.6497963
151 152 153 154 155
433.8461202 437.6049224 441.4921957 445.6572659 450.1189983
156 157 158 159 160
454.6051055 459.5516784 463.8801444 468.4506181 473.4785488
161 162 163 164 165
478.4336291 483.4095560 487.8930239 492.2709940 496.6538382
166 167 168 169 170
500.8238329 505.0830309 509.2028432 512.7422071 516.0500015
171 172 173 174 175
519.4046710 522.5750552 525.8063001 529.4559519 533.5254526
176 177 178 179 180
537.4638424 541.4933868 545.2688001 548.8175588 552.3513852
181 182
555.4754727 558.5767622 cumsum(tapply(all_data$all_pre_data$RECO_predict_treatment_q025,
round(all_data$all_pre_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
-0.007277171 -0.027420571 0.162427330 0.170927926
5 6 7 8
0.255886849 0.387592957 0.392003364 0.405303525
9 10 11 12
0.490908274 0.642313565 0.881173903 1.142142922
13 14 15 16
1.298393391 1.730681203 2.186837127 2.345360486
17 18 19 20
2.645065632 2.817070924 3.582834699 4.040777658
21 22 23 24
4.490610763 4.934296434 5.422109595 5.901317550
25 26 27 28
6.294411082 6.398757292 9.042087144 11.188918911
29 30 31 32
12.884535491 13.895874293 16.827140874 20.706803824
33 34 35 36
24.542525503 29.080266183 33.625165215 38.226182864
37 38 39 40
43.071792650 47.643049508 51.462957422 55.234947397
41 42 43 44
59.221684159 63.801168420 68.278106763 72.305858147
45 46 47 48
75.288082791 77.924501904 80.191533154 82.854125480
49 50 51 52
86.034286716 88.748439875 91.415009012 94.231918551
53 54 55 56
96.743024889 99.048720459 101.459927745 104.083488529
57 58 59 60
106.343510984 108.451765343 110.891213793 113.831245593
61 62 63 64
116.876107871 119.861811106 122.847228958 125.496819043
65 66 67 68
128.410897977 131.035149404 133.410928189 135.279815023
69 70 71 72
137.844266020 140.215002833 142.276480132 144.803302819
73 74 75 76
146.777348744 148.947034117 151.155436850 152.951866298
77 78 79 80
154.786521648 156.064031018 157.653929352 159.360181600
81 82 83 84
161.108696903 163.807355608 166.906906337 169.526066420
85 86 87 88
172.465565761 176.067914617 179.776294182 183.084833459
89 90 91 92
186.451128051 189.356350548 192.636520541 195.774313235
93 94 95 96
198.618007937 201.680619443 203.721215898 205.172635859
97 98 99 100
206.922936124 209.182345416 211.675106712 214.077509903
101 102 103 104
216.379777977 218.863542628 221.904861941 224.989774623
105 106 107 108
227.637692523 230.405878090 233.341898393 236.665998444
109 110 111 112
239.737378014 242.956895203 245.551466892 248.225137519
113 114 115 116
251.016352428 254.234840739 257.157285937 259.932613293
117 118 119 120
263.071060785 265.936466490 268.928453700 272.070096367
121 122 123 124
275.177312308 277.190294502 279.042176336 281.641876281
125 126 127 128
284.183746950 286.524175619 288.528204931 289.557550183
129 130 131 132
289.657157050 289.868946809 290.464133518 291.588287504
133 134 135 136
292.676141849 293.468145070 293.439363880 293.397182560
137 138 139 140
293.423803423 292.378066105 290.982444699 289.623938203
141 142 143 144
288.236031303 286.573633866 284.954966093 283.353317422
145 146 147 148
281.399941115 279.149564278 277.117303163 275.470251343
149 150 151 152
274.527896067 274.214774074 274.894550565 276.799862371
153 154 155 156
279.343697907 282.433416114 285.986674233 289.630500337
157 158 159 160
293.415551276 296.588874663 299.897249668 303.691207076
161 162 163 164
307.474495030 311.283935492 314.649578964 317.881041506
165 166 167 168
321.030051350 323.843620951 326.710225769 330.099840295
169 170 171 172
333.149055567 335.882566712 338.658761277 341.380356821
173 174 175 176
343.825578042 346.390444948 349.181522419 351.572647843
177 178 179 180
354.148785764 356.878874942 359.226024406 361.376077202
181 182
363.617417331 365.620890428 cumsum(tapply(all_data$all_pre_data$RECO_predict_treatment_q975 ,
round(all_data$all_pre_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4 5
1.403028 2.941851 4.728503 6.291975 7.973494
6 7 8 9 10
9.516943 11.026318 12.566980 14.037729 15.614468
11 12 13 14 15
17.459448 19.006937 20.487570 21.802912 23.292152
16 17 18 19 20
24.789570 26.547374 28.260832 30.295325 32.343311
21 22 23 24 25
34.412567 36.746071 39.401644 42.299298 45.295195
26 27 28 29 30
48.083038 52.705609 56.217162 59.183508 61.783790
31 32 33 34 35
66.930727 73.172344 79.430968 85.945354 92.199962
36 37 38 39 40
98.298392 104.396342 110.124988 115.266649 120.331410
41 42 43 44 45
125.455048 131.087319 136.504142 141.533540 145.847865
46 47 48 49 50
149.867335 153.617983 157.764871 162.363635 166.495615
51 52 53 54 55
170.544556 174.644337 178.346638 181.758966 185.162642
56 57 58 59 60
188.831777 192.275776 195.691910 199.348433 203.640980
61 62 63 64 65
208.051900 212.379266 216.542155 220.250412 224.250342
66 67 68 69 70
227.914291 231.373602 234.577247 238.009409 241.450302
71 72 73 74 75
244.564490 247.916495 250.792142 253.906654 257.376291
76 77 78 79 80
260.219968 263.092248 265.816066 268.554746 271.255108
81 82 83 84 85
274.033139 277.745886 281.925704 285.750153 290.045157
86 87 88 89 90
295.041073 300.485595 305.028712 309.571099 313.795282
91 92 93 94 95
318.491780 323.427218 327.983783 332.437213 336.271736
96 97 98 99 100
339.313830 342.604257 346.265485 350.183133 354.005989
101 102 103 104 105
357.810756 362.120582 367.055949 371.601123 375.551104
106 107 108 109 110
379.567804 383.875839 388.555827 393.231985 397.888017
111 112 113 114 115
402.080397 406.288840 410.773657 415.387220 419.697632
116 117 118 119 120
424.156721 429.161152 433.862300 438.711922 443.673103
121 122 123 124 125
448.024778 451.840010 456.193977 461.200064 466.039513
126 127 128 129 130
470.269038 474.277988 477.957326 481.691870 485.712294
131 132 133 134 135
490.346401 494.989241 499.571606 503.631667 507.458554
136 137 138 139 140
511.369235 515.169854 518.990323 522.925684 526.976766
141 142 143 144 145
531.201157 535.456094 539.850938 544.167088 548.339123
146 147 148 149 150
552.420636 556.522086 560.671364 565.101629 569.734672
151 152 153 154 155
574.613700 579.643321 584.514686 589.576367 594.810062
156 157 158 159 160
600.007303 606.109535 611.593099 617.442986 623.779055
161 162 163 164 165
630.034746 636.307542 642.014950 647.573087 653.164134
166 167 168 169 170
658.587848 664.146927 669.000885 673.071155 677.020518
171 172 173 174 175
681.048597 684.668123 688.751162 693.346118 698.549279
176 177 178 179 180
703.812193 709.004135 713.678316 718.243549 722.950858
181 182
726.912607 731.019135 #Cumlative sum GPP treatment side pre compost application with CI’s
cumsum(tapply(all_data$all_pre_data$GPP_treatment_mean ,
round(all_data$all_pre_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4 5
-1.172758 -2.286897 -3.687987 -5.567412 -7.527279
6 7 8 9 10
-9.526040 -11.453581 -13.559381 -15.377789 -17.107642
11 12 13 14 15
-18.762423 -20.575497 -21.982607 -22.862714 -24.597608
16 17 18 19 20
-26.122108 -27.236319 -28.295306 -29.108033 -29.833185
21 22 23 24 25
-30.835714 -31.803099 -32.787574 -33.849187 -35.167536
26 27 28 29 30
-35.570224 -35.509151 -35.264476 -35.695115 -34.992359
31 32 33 34 35
-37.275356 -40.626953 -44.497475 -44.047965 -44.077237
36 37 38 39 40
-44.781270 -45.265093 -46.127913 -47.038300 -48.022949
41 42 43 44 45
-48.235892 -48.715257 -49.062693 -51.064184 -53.143424
46 47 48 49 50
-55.206254 -56.943634 -58.259911 -59.461747 -60.540665
51 52 53 54 55
-61.091872 -61.724460 -63.374495 -65.088118 -66.830979
56 57 58 59 60
-69.304722 -71.417867 -73.557460 -75.036464 -77.627399
61 62 63 64 65
-79.749257 -81.949424 -84.173105 -87.027826 -89.407219
66 67 68 69 70
-92.019049 -94.529769 -97.649420 -99.907659 -101.286398
71 72 73 74 75
-103.448930 -105.949206 -108.150360 -110.705429 -113.323115
76 77 78 79 80
-116.106458 -117.808180 -121.010089 -124.305642 -127.172116
81 82 83 84 85
-129.613918 -132.129049 -135.798443 -138.909822 -142.996935
86 87 88 89 90
-147.042594 -149.991122 -153.398711 -155.395653 -158.687658
91 92 93 94 95
-161.814733 -164.060181 -166.501500 -169.554807 -172.861832
96 97 98 99 100
-175.900001 -178.771866 -181.794318 -184.964065 -188.412015
101 102 103 104 105
-192.028752 -195.050106 -198.025798 -201.584294 -206.237345
106 107 108 109 110
-210.352702 -214.429666 -218.947629 -223.144819 -227.079132
111 112 113 114 115
-231.393139 -235.477151 -239.209663 -243.281693 -247.765155
116 117 118 119 120
-252.033221 -256.032661 -260.188466 -264.167918 -268.041618
121 122 123 124 125
-272.490161 -276.358612 -280.174026 -284.006340 -287.868207
126 127 128 129 130
-292.322295 -295.996131 -299.475394 -302.740481 -306.908127
131 132 133 134 135
-310.391621 -314.567824 -319.038977 -324.062441 -328.127607
136 137 138 139 140
-332.289930 -337.309774 -342.488423 -346.971228 -352.454697
141 142 143 144 145
-358.290249 -363.425973 -368.798657 -374.672044 -380.490870
146 147 148 149 150
-387.359695 -394.378184 -401.641735 -407.030604 -412.582669
151 152 153 154 155
-419.094246 -425.721196 -432.566856 -438.832619 -444.852627
156 157 158 159 160
-449.776699 -453.853799 -458.414005 -463.966665 -469.172016
161 162 163 164 165
-473.747432 -478.881896 -484.312320 -489.786153 -496.492072
166 167 168 169 170
-503.142873 -509.322207 -514.810284 -521.091021 -526.330802
171 172 173 174 175
-531.387469 -535.695459 -539.375925 -543.461388 -547.496781
176 177 178 179 180
-551.618288 -555.440094 -559.193413 -562.940795 -566.405582
181 182
-569.294197 -572.407919 cumsum(tapply(all_data$all_pre_data$GPP_treatment_q025,
round(all_data$all_pre_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4 5
-2.060209 -3.841801 -5.478946 -7.504117 -9.683201
6 7 8 9 10
-11.845944 -14.144844 -16.567171 -18.742142 -20.802938
11 12 13 14 15
-22.465036 -24.531063 -26.122263 -27.449358 -29.475675
16 17 18 19 20
-31.230373 -32.499923 -33.795179 -35.113951 -36.336810
21 22 23 24 25
-37.971325 -39.516520 -40.975698 -42.368248 -43.981641
26 27 28 29 30
-44.470207 -44.906294 -44.980240 -45.667558 -45.368595
31 32 33 34 35
-49.749169 -55.422333 -62.242474 -62.116187 -62.374628
36 37 38 39 40
-63.234042 -63.874714 -64.963093 -66.621169 -67.824287
41 42 43 44 45
-68.369679 -68.924922 -69.574564 -71.790161 -74.087647
46 47 48 49 50
-76.568076 -78.936941 -80.085193 -81.632043 -82.936230
51 52 53 54 55
-83.956944 -85.120155 -87.016400 -88.821252 -90.931533
56 57 58 59 60
-93.601421 -96.133513 -98.555035 -100.196592 -103.393526
61 62 63 64 65
-106.168402 -108.690686 -111.360771 -114.580593 -117.152089
66 67 68 69 70
-120.176675 -123.032858 -126.486014 -129.031595 -131.046014
71 72 73 74 75
-133.650184 -136.457796 -138.919832 -142.108633 -144.930257
76 77 78 79 80
-148.014410 -150.628697 -154.331950 -158.107620 -161.521130
81 82 83 84 85
-164.525142 -167.522600 -171.675052 -175.520638 -180.027391
86 87 88 89 90
-184.571231 -188.211793 -191.538864 -194.777381 -198.565482
91 92 93 94 95
-202.144371 -205.351466 -208.541533 -212.076539 -215.941355
96 97 98 99 100
-219.445429 -222.657385 -226.096976 -229.587652 -233.407976
101 102 103 104 105
-237.467993 -240.956936 -244.431818 -248.467702 -253.585112
106 107 108 109 110
-258.255329 -263.046197 -267.660170 -272.433865 -277.051091
111 112 113 114 115
-281.870397 -286.307560 -290.481222 -295.112462 -300.040743
116 117 118 119 120
-304.736183 -309.147885 -313.462544 -317.683719 -321.674579
121 122 123 124 125
-326.518955 -330.807491 -335.062988 -339.193071 -343.683951
126 127 128 129 130
-348.905490 -353.490929 -357.841932 -362.069803 -367.385285
131 132 133 134 135
-371.876972 -377.153158 -382.857557 -389.316247 -394.668739
136 137 138 139 140
-400.885750 -408.296775 -415.049384 -420.835932 -427.646177
141 142 143 144 145
-434.711077 -441.000340 -447.592925 -454.871766 -462.312759
146 147 148 149 150
-471.302216 -480.455388 -489.725670 -496.710370 -503.737124
151 152 153 154 155
-511.425480 -519.095406 -526.698878 -533.615749 -540.100375
156 157 158 159 160
-545.536411 -550.528780 -556.716745 -563.722744 -570.368001
161 162 163 164 165
-576.795411 -584.242405 -592.449061 -600.488629 -609.268302
166 167 168 169 170
-617.815080 -625.271369 -631.808167 -637.971330 -643.321441
171 172 173 174 175
-648.689066 -652.804254 -657.307645 -661.130969 -664.975312
176 177 178 179 180
-670.225130 -674.842913 -678.715247 -682.411286 -686.361721
181 182
-689.822315 -693.873419 cumsum(tapply(all_data$all_pre_data$GPP_treatment_q975 ,
round(all_data$all_pre_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
-0.3964369 -1.1575437 -2.0984160 -3.4235323
5 6 7 8
-4.9167801 -6.5118630 -8.1847238 -9.8432361
9 10 11 12
-11.5118739 -13.0817124 -14.2998988 -15.7959447
13 14 15 16
-16.9254748 -17.7263608 -19.2278404 -20.3762990
17 18 19 20
-21.2133884 -22.0882560 -22.4616833 -22.7153401
21 22 23 24
-23.1672601 -23.6308873 -24.1770343 -24.9185842
25 26 27 28
-25.9723140 -26.2810171 -26.1160062 -25.5112829
29 30 31 32
-25.5970223 -24.4316482 -24.9096607 -25.9215857
33 34 35 36
-27.2140344 -26.3738717 -26.0632958 -26.2309487
37 38 39 40
-26.3697739 -26.8362906 -27.9267805 -28.6353885
41 42 43 44
-28.6174640 -28.5883136 -28.7574689 -30.4199084
45 46 47 48
-32.2314331 -33.9456345 -35.4571251 -36.4849428
49 50 51 52
-37.4105159 -38.1099540 -38.3909093 -38.8575113
53 54 55 56
-40.2677175 -41.6611789 -43.0951509 -44.9310574
57 58 59 60
-46.8088987 -48.6445761 -49.6702406 -51.8199852
61 62 63 64
-53.7432302 -55.6843015 -57.6973189 -60.0828402
65 66 67 68
-61.8474991 -64.1497275 -66.2873891 -69.0979532
69 70 71 72
-71.0117441 -71.6645680 -73.5191003 -75.5280841
73 74 75 76
-77.5285107 -80.1954018 -82.0133883 -84.5398746
77 78 79 80
-85.2912426 -88.0696849 -90.8617749 -93.2766626
81 82 83 84
-95.3134375 -97.4736800 -100.5967810 -103.2335164
85 86 87 88
-106.3485524 -109.5810098 -112.0020503 -114.5797183
89 90 91 92
-116.6511114 -119.0722525 -121.1408605 -122.6876580
93 94 95 96
-124.3140354 -126.5133504 -129.1932442 -131.6770793
97 98 99 100
-134.0462188 -136.7093722 -139.4450236 -142.3864578
101 102 103 104
-145.4505403 -147.9330332 -150.3644731 -153.5104877
105 106 107 108
-157.5449962 -161.1886646 -164.9097778 -168.6199882
109 110 111 112
-172.3022783 -175.8801096 -179.8914557 -183.5807859
113 114 115 116
-186.9333999 -190.6163616 -194.5528730 -198.4338687
117 118 119 120
-202.0518265 -205.8784542 -209.6661488 -213.2570183
121 122 123 124
-217.2750255 -220.6553124 -223.8279817 -227.2696231
125 126 127 128
-230.6110288 -233.7351195 -236.1278288 -238.3177762
129 130 131 132
-240.3431526 -243.1374660 -245.3303978 -248.0749253
133 134 135 136
-251.1522245 -254.6538091 -257.3041191 -259.5622612
137 138 139 140
-261.7405832 -265.7161314 -269.0463956 -273.3115212
141 142 143 144
-277.9706605 -282.0107511 -286.2331742 -290.6775085
145 146 147 148
-294.9776672 -300.0672792 -305.2443120 -310.7181448
149 150 151 152
-314.5848644 -318.7532852 -323.9902136 -329.5046247
153 154 155 156
-335.5451909 -341.3053459 -346.6278860 -350.6188518
157 158 159 160
-353.7218028 -356.8642941 -361.0727806 -364.9466160
161 162 163 164
-367.8296134 -370.9211969 -373.6402428 -376.7418826
165 166 167 168
-381.6613751 -386.8557146 -391.9994977 -397.1901618
169 170 171 172
-402.7723193 -407.7936484 -412.8521353 -416.4604372
173 174 175 176
-420.7489657 -424.3914921 -428.0996956 -431.5725765
177 178 179 180
-434.8532009 -438.6284349 -442.2702533 -445.2264307
181 182
-447.3385010 -449.1950554 #Cumlative sum Reco Treatment side post compost application with CI’s
#Reco Cumsum
cumsum(tapply(all_data$all_post_data$RECO_predict_treatment_mean,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4 5
1.499663 2.826351 3.845111 4.687869 5.479498
6 7 8 9 10
6.108483 6.752767 7.401349 8.101795 9.217710
11 12 13 14 15
10.871573 12.305658 14.103919 15.823695 17.487732
16 17 18 19 20
19.718671 22.632598 26.067018 28.964851 31.460394
21 22 23 24 25
33.470141 34.854128 36.434829 37.641961 38.864687
26 27 28 29 30
39.722151 40.534277 40.958386 41.523885 42.443884
31 32 33 34 35
43.718507 44.864157 45.988180 47.048214 48.001388
36 37 38 39 40
48.859335 49.805350 50.810266 51.721616 53.086570
41 42 43 44 45
54.991124 57.801948 60.956621 64.163387 67.330953
46 47 48 49 50
70.404599 73.742369 76.485757 78.710268 80.852365
51 52 53 54 55
82.568768 84.649161 86.642957 88.508072 90.502796
56 57 58 59 60
92.844607 94.860365 96.831831 98.762958 100.926174
61 62 63 64 65
103.094530 105.343467 107.664170 109.792966 112.424864
66 67 68 69 70
115.009954 117.573011 120.187748 122.640971 125.032291
71 72 73 74 75
127.163827 129.205089 131.165721 133.245873 135.578021
76 77 78 79 80
137.629478 139.751228 141.972396 144.153397 146.245853
81 82 83 84 85
148.124345 149.693694 151.402988 153.348736 155.382382
86 87 88 89 90
157.679989 160.277464 162.005331 164.340508 166.524295
91 92 93 94 95
169.318821 171.993338 174.580130 177.362540 179.683123
96 97 98 99 100
181.979620 184.330330 186.775889 189.058834 191.327781
101 102 103 104 105
194.006017 196.922402 199.989372 203.118981 206.558811
106 107 108 109 110
209.542209 213.043972 216.119111 218.923380 221.841895
111 112 113 114 115
225.213938 228.444870 231.821784 235.647553 239.252461
116 117 118 119 120
241.898048 244.033413 245.942491 247.684757 249.842190
121 122 123 124 125
251.911904 254.078196 256.116809 258.074410 260.083955
126 127 128 129 130
262.272955 264.393665 266.655586 268.758181 270.989149
131 132 133 134 135
273.274859 275.693367 278.323540 281.498632 284.863409
136 137 138 139 140
288.148743 291.364946 295.195097 299.040759 302.539642
141 142 143 144 145
305.593400 308.663198 311.506288 314.341186 317.278114
146 147 148 149 150
320.177636 323.191391 326.430865 329.710231 333.134980
151 152 153 154 155
336.661753 340.001255 342.903576 345.423923 347.734996
156 157 158 159 160
349.924628 352.062817 354.179674 356.138110 357.949324
161 162 163 164 165
359.763253 361.900246 364.339386 366.873801 369.398134
166 167 168 169 170
371.593218 373.263541 374.825916 376.451514 378.023291
171 172 173 174 175
379.586577 381.095287 382.477292 383.662044 384.673094
176 177 178 179 180
385.655356 386.542018 387.435439 388.535592 389.932487
181
391.635500 #lower bound CI
cumsum(tapply(all_data$all_post_data$RECO_predict_treatment_q025,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
-0.026378513 -0.038808584 0.008638873 0.016775002
5 6 7 8
-0.081283452 0.064567926 0.307353647 0.453653837
9 10 11 12
0.497551556 0.874275883 1.958686500 2.715945215
13 14 15 16
3.874645134 4.960170390 5.922627488 7.469565520
17 18 19 20
9.688541122 12.444921806 14.591832688 16.366256420
21 22 23 24
17.721436866 18.512090442 19.585041639 20.225199622
25 26 27 28
20.967715536 21.276171076 21.494266601 21.211331716
29 30 31 32
21.141521856 21.443783658 22.067417718 22.582649474
33 34 35 36
23.112510392 23.521040325 23.847832950 24.065523913
37 38 39 40
24.184362081 24.374452117 24.655265352 25.404982543
41 42 43 44
26.887971146 29.232860733 31.820470930 34.368224532
45 46 47 48
36.784178852 38.932614023 41.389991426 43.330327565
49 50 51 52
44.827131455 46.192707101 47.020820533 48.486871255
53 54 55 56
49.903130297 51.150786612 52.639159270 54.486878101
57 58 59 60
55.959365076 57.316641894 58.447189600 59.800606354
61 62 63 64
61.393592583 62.992554207 64.734559316 66.185533551
65 66 67 68
68.147712886 70.121512092 72.004707784 73.963148512
69 70 71 72
75.757873273 77.402790692 78.794225429 80.030835224
73 74 75 76
81.209394699 82.466400594 83.988131294 85.273328272
77 78 79 80
86.588328503 87.893704807 89.096922113 90.584738782
81 82 83 84
91.841658808 92.831045614 94.070767374 95.544126305
85 86 87 88
97.057917731 98.650197859 99.955192896 99.595979802
89 90 91 92
98.949536724 96.521159249 98.414521677 100.361056668
93 94 95 96
102.247747193 104.413539689 106.179256620 107.811559500
97 98 99 100
109.396142736 111.010148568 112.379334643 113.693015964
101 102 103 104
115.699729725 117.999584060 120.565533150 123.147285735
105 106 107 108
126.076538958 128.408754721 131.305098405 133.614994952
109 110 111 112
135.451085121 137.433004922 140.148920135 142.745606247
113 114 115 116
145.249269660 147.994970496 150.387463129 152.102120559
117 118 119 120
153.393078811 154.580610796 155.639167556 157.097568047
121 122 123 124
158.449648908 159.900875750 161.297655989 162.609706075
125 126 127 128
163.906295189 165.544049668 167.091042153 168.942178861
129 130 131 132
170.462334913 171.973295780 173.130220219 174.581793503
133 134 135 136
176.644012081 179.319009299 182.049591271 184.518017335
137 138 139 140
187.036592826 190.192704934 193.345931767 196.146620194
141 142 143 144
198.468473157 200.990756684 203.165635182 205.247655813
145 146 147 148
207.528333811 209.581133494 211.563575834 213.743869644
149 150 151 152
216.070302490 218.510737872 221.029399439 223.381062927
153 154 155 156
225.523912961 227.551907904 229.322816543 231.019451211
157 158 159 160
232.501815862 234.056391658 235.337673992 236.559014821
161 162 163 164
237.580769736 238.914559888 240.602215473 242.639923413
165 166 167 168
244.623849719 246.253654395 247.247642136 248.076558722
169 170 171 172
248.824308565 249.795379719 250.947248564 251.946307438
173 174 175 176
252.944101888 253.650983436 254.288475518 255.002183219
177 178 179 180
255.422098092 255.709571538 256.037843235 256.568936928
181
257.477449545 #upper bound CI
cumsum(tapply(all_data$all_post_data$RECO_predict_treatment_q975 ,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4 5
2.897563 5.459978 7.400499 9.039906 10.668182
6 7 8 9 10
11.820499 12.881448 13.966152 15.288765 17.103327
11 12 13 14 15
19.261921 21.320272 23.707761 26.026667 28.374220
16 17 18 19 20
31.256786 34.898494 39.028750 42.661308 45.869725
21 22 23 24 25
48.533890 50.513975 52.611936 54.354350 56.070321
26 27 28 29 30
57.504271 58.910105 59.988992 61.171496 62.699733
31 32 33 34 35
64.609548 66.369579 68.097916 69.843021 71.467080
36 37 38 39 40
73.005845 74.785647 76.604813 78.190979 80.187305
41 42 43 44 45
82.525014 85.788500 89.496540 93.314283 97.178447
46 47 48 49 50
101.037578 105.196319 108.722244 111.714276 114.812884
51 52 53 54 55
117.715899 120.431028 123.041079 125.665150 128.226814
56 57 58 59 60
131.061717 133.585092 136.127955 138.817372 142.001400
61 62 63 64 65
144.749104 147.681934 150.591402 153.453917 156.783314
66 67 68 69 70
160.005896 163.325374 166.623099 169.744995 172.924932
71 72 73 74 75
175.835648 178.721145 181.415090 184.264027 187.367758
76 77 78 79 80
190.166748 193.109887 196.237586 199.379442 202.041881
81 82 83 84 85
204.529512 206.682703 208.854622 211.275786 213.815587
86 87 88 89 90
216.925547 221.362424 225.772484 230.614194 236.280289
91 92 93 94 95
240.099634 243.540659 246.811102 250.180577 253.013601
96 97 98 99 100
255.953236 259.075021 262.345636 265.541393 268.741887
101 102 103 104 105
272.023324 275.519100 279.075996 282.749032 286.729383
106 107 108 109 110
290.388004 294.563050 298.451401 302.223474 306.036464
111 112 113 114 115
310.061674 313.950912 318.361004 323.447854 328.467737
116 117 118 119 120
332.034708 335.099046 337.846868 340.290248 343.130578
121 122 123 124 125
345.920848 348.777554 351.419880 354.007454 356.676903
126 127 128 129 130
359.406352 362.103145 364.791569 367.455262 370.524010
131 132 133 134 135
373.993470 377.301407 380.493122 384.212895 388.362889
136 137 138 139 140
392.515349 396.444313 400.984480 405.559945 409.765223
141 142 143 144 145
413.537390 417.151888 420.673923 424.266381 427.873857
146 147 148 149 150
431.654722 435.766169 440.131305 444.440429 448.928719
151 152 153 154 155
453.528588 457.887406 461.587740 464.605012 467.436742
156 157 158 159 160
470.117308 472.930503 475.628003 478.328004 480.739631
161 162 163 164 165
483.361279 486.296251 489.516330 492.557386 495.649136
166 167 168 169 170
498.407919 500.730156 502.968443 505.389336 507.520158
171 172 173 174 175
509.493535 511.518269 513.286857 514.952852 516.345756
176 177 178 179 180
517.627558 519.029823 520.569331 522.453764 524.679099
181
527.176959 #Cumulative sum GPP treament side post compost application with CI’s
#GPP Cumsum
cumsum(tapply(all_data$all_post_data$GPP_treatment_mean,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
-0.7700035 -1.3419130 -1.9694535 -2.4072138
5 6 7 8
-2.4078975 -3.2921129 -4.4684425 -5.6891135
9 10 11 12
-7.5057843 -8.9501545 -10.0425270 -11.4432422
13 14 15 16
-12.0100421 -13.1301796 -14.3482762 -15.5807345
17 18 19 20
-16.4394424 -15.9677393 -17.5342661 -19.1912917
21 22 23 24
-20.5040607 -21.4567661 -22.5659322 -23.6993379
25 26 27 28
-24.9368876 -26.3394104 -27.9992506 -29.4679940
29 30 31 32
-30.9120490 -32.2438415 -33.6437545 -35.0129053
33 34 35 36
-36.4010249 -37.4892691 -38.6466842 -39.7328361
37 38 39 40
-40.5474272 -41.4212815 -42.3333279 -43.3393102
41 42 43 44
-44.0974321 -44.8520530 -45.0975702 -46.5071452
45 46 47 48
-47.4207864 -48.4001037 -48.8133911 -50.0453248
49 50 51 52
-51.4588007 -52.8775544 -54.0299386 -55.6216365
53 54 55 56
-57.4761918 -58.9699481 -59.8107658 -60.5816555
57 58 59 60
-61.7450187 -62.4018161 -63.5739972 -64.6892335
61 62 63 64
-65.6885759 -66.5842605 -66.7355751 -67.1620454
65 66 67 68
-67.2101776 -68.8136404 -70.1317139 -71.9060253
69 70 71 72
-73.2971337 -75.6371896 -77.6528515 -79.9351937
73 74 75 76
-81.3179424 -82.6325103 -84.6189665 -86.7379256
77 78 79 80
-88.5557244 -90.2892093 -92.0484714 -94.2176276
81 82 83 84
-95.8655955 -97.4103777 -98.2052392 -99.5558951
85 86 87 88
-100.4090866 -102.3872300 -103.6417659 -104.5540600
89 90 91 92
-105.4777659 -107.1146959 -109.3465543 -110.9935077
93 94 95 96
-112.2181777 -113.8024786 -115.1999076 -117.3294000
97 98 99 100
-119.0329261 -120.9798588 -123.1507313 -125.5014474
101 102 103 104
-127.5107655 -130.5281675 -132.1048127 -135.2275343
105 106 107 108
-138.0060132 -141.0021617 -143.7663732 -147.4814709
109 110 111 112
-151.1992915 -154.6345999 -157.2552513 -161.4009831
113 114 115 116
-166.1411713 -170.0358284 -172.3281581 -174.4987334
117 118 119 120
-177.5280977 -180.4938936 -183.8438818 -186.9958320
121 122 123 124
-189.9409206 -192.6515801 -195.5637503 -198.4913809
125 126 127 128
-201.4100275 -204.9433397 -208.3147977 -211.6320639
129 130 131 132
-215.0820456 -219.1243348 -222.5085404 -226.4937821
133 134 135 136
-231.8464686 -237.1679789 -242.7113589 -248.0016578
137 138 139 140
-253.0874697 -257.0860896 -262.1403137 -267.6059798
141 142 143 144
-272.7311034 -277.6397984 -282.0425273 -286.0426266
145 146 147 148
-290.4171600 -293.9588313 -297.2177263 -300.5763476
149 150 151 152
-304.1812017 -307.7126076 -310.7400736 -313.5113244
153 154 155 156
-316.3611262 -319.8029005 -322.9775120 -326.2429071
157 158 159 160
-329.2948192 -332.2568219 -334.9140274 -337.9865853
161 162 163 164
-340.5326856 -343.3277957 -346.3134132 -349.5629129
165 166 167 168
-352.8374116 -355.8346490 -358.2919360 -360.5667714
169 170 171 172
-362.5858366 -364.6012448 -367.5742751 -369.9264703
173 174 175 176
-372.5330901 -374.9517528 -376.8840409 -378.3271003
177 178 179 180
-380.4300326 -382.3501178 -384.0721996 -385.7921479
181
-387.6552809 ########GPP comparison using Reco_predict######
#making the comparison GPP variable
all_data$all_post_data$GPP_treatment_with_pred_Reco <- (all_data$all_post_data$NEE_filled_treatment_mean - all_data$all_post_data$RECO_predict_treatment_mean)
all_data$all_post_data$GPP_treatment_with_pred_Reco [(all_data$all_post_data$Rg <= 10 )]<-0
cumsum(tapply(all_data$all_post_data$GPP_treatment_with_pred_Reco,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
-0.7700035 -1.3419130 -1.9694535 -2.4072138
5 6 7 8
-2.4078975 -3.2921129 -4.4684425 -5.6891135
9 10 11 12
-7.5057843 -8.9501545 -10.0425270 -11.4432422
13 14 15 16
-12.0100421 -13.1301796 -14.3482762 -15.5807345
17 18 19 20
-16.4394424 -15.9677393 -17.5342661 -19.1912917
21 22 23 24
-20.5040607 -21.4567661 -22.5659322 -23.6993379
25 26 27 28
-24.9368876 -26.3394104 -27.9992506 -29.4679940
29 30 31 32
-30.9120490 -32.2438415 -33.6437545 -35.0129053
33 34 35 36
-36.4010249 -37.4892691 -38.6466842 -39.7328361
37 38 39 40
-40.5474272 -41.4212815 -42.3333279 -43.3393102
41 42 43 44
-44.0974321 -44.8520530 -45.0975702 -46.5071452
45 46 47 48
-47.4207864 -48.4001037 -48.8133911 -50.0453248
49 50 51 52
-51.4588007 -52.8775544 -54.0299386 -55.6216365
53 54 55 56
-57.4761918 -58.9699481 -59.8107658 -60.5816555
57 58 59 60
-61.7450187 -62.4018161 -63.5739972 -64.6892335
61 62 63 64
-65.6885759 -66.5842605 -66.7355751 -67.1620454
65 66 67 68
-67.2101776 -68.8136404 -70.1317139 -71.9060253
69 70 71 72
-73.2971337 -75.6371896 -77.6528515 -79.9351937
73 74 75 76
-81.3179424 -82.6325103 -84.6189665 -86.7379256
77 78 79 80
-88.5557244 -90.2892093 -92.0484714 -94.2176276
81 82 83 84
-95.8655955 -97.4103777 -98.2052392 -99.5558951
85 86 87 88
-100.4090866 -102.3872300 -103.6417659 -104.5540600
89 90 91 92
-105.4777659 -107.1146959 -109.3465543 -110.9935077
93 94 95 96
-112.2181777 -113.8024786 -115.1999076 -117.3294000
97 98 99 100
-119.0329261 -120.9798588 -123.1507313 -125.5014474
101 102 103 104
-127.5107655 -130.5281675 -132.1048127 -135.2275343
105 106 107 108
-138.0060132 -141.0021617 -143.7663732 -147.4814709
109 110 111 112
-151.1992915 -154.6345999 -157.2552513 -161.4009831
113 114 115 116
-166.1411713 -170.0358284 -172.3281581 -174.4987334
117 118 119 120
-177.5280977 -180.4938936 -183.8438818 -186.9958320
121 122 123 124
-189.9409206 -192.6515801 -195.5637503 -198.4913809
125 126 127 128
-201.4100275 -204.9433397 -208.3147977 -211.6320639
129 130 131 132
-215.0820456 -219.1243348 -222.5085404 -226.4937821
133 134 135 136
-231.8464686 -237.1679789 -242.7113589 -248.0016578
137 138 139 140
-253.0874697 -257.0860896 -262.1403137 -267.6059798
141 142 143 144
-272.7311034 -277.6397984 -282.0425273 -286.0426266
145 146 147 148
-290.4171600 -293.9588313 -297.2177263 -300.5763476
149 150 151 152
-304.1812017 -307.7126076 -310.7400736 -313.5113244
153 154 155 156
-316.3611262 -319.8029005 -322.9775120 -326.2429071
157 158 159 160
-329.2948192 -332.2568219 -334.9140274 -337.9865853
161 162 163 164
-340.5326856 -343.3277957 -346.3134132 -349.5629129
165 166 167 168
-352.8374116 -355.8346490 -358.2919360 -360.5667714
169 170 171 172
-362.5858366 -364.6012448 -367.5742751 -369.9264703
173 174 175 176
-372.5330901 -374.9517528 -376.8840409 -378.3271003
177 178 179 180
-380.4300326 -382.3501178 -384.0721996 -385.7921479
181
-387.6552809 #essentially the same. -387.73 with this v.s -387.66
#lower bound CI
cumsum(tapply(all_data$all_post_data$GPP_treatment_q025,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
-0.4488801 -0.7189981 -1.2210473 -1.6191176
5 6 7 8
-1.5750823 -2.5323049 -3.7998070 -5.3610026
9 10 11 12
-7.4293656 -9.0054254 -10.2329938 -11.7304555
13 14 15 16
-12.3938476 -13.6735912 -14.8851495 -16.1407193
17 18 19 20
-16.6850543 -17.6141141 -19.2772019 -21.0467761
21 22 23 24
-22.5504041 -23.8153472 -24.9967423 -26.1095425
25 26 27 28
-27.5546324 -29.1961757 -31.1117764 -32.8745977
29 30 31 32
-34.5448340 -36.2189352 -37.8506508 -39.4615683
33 34 35 36
-41.0595111 -42.4193555 -43.7747757 -45.0432533
37 38 39 40
-45.9049089 -46.8329178 -47.9909060 -49.0558647
41 42 43 44
-49.9731925 -51.0688520 -51.8643157 -53.7755230
45 46 47 48
-55.1077593 -56.4553226 -57.4560312 -59.0735411
49 50 51 52
-60.9186274 -62.6049132 -64.2428761 -66.2893017
53 54 55 56
-68.6115726 -70.3983422 -71.2451264 -72.3429964
57 58 59 60
-73.7119802 -74.5922039 -75.9837332 -77.3092926
61 62 63 64
-78.4136108 -79.4163119 -79.8070338 -80.4509862
65 66 67 68
-80.6400851 -82.4092184 -83.8928115 -85.8805984
69 70 71 72
-87.5494333 -90.1137937 -92.3449411 -94.7835311
73 74 75 76
-96.3401397 -97.9127146 -100.0821631 -102.4587943
77 78 79 80
-104.3964369 -106.2319013 -108.2639533 -110.9076835
81 82 83 84
-112.9210177 -114.8144606 -116.1182439 -117.5908328
85 86 87 88
-118.2623091 -120.2212955 -121.4173486 -122.1023745
89 90 91 92
-124.3970041 -126.2060779 -129.8940893 -131.8487188
93 94 95 96
-133.2993797 -134.6851549 -136.4250393 -138.7653769
97 98 99 100
-140.6185613 -142.7578630 -145.1737661 -147.9908681
101 102 103 104
-149.9547860 -152.8422663 -155.0520981 -158.4823094
105 106 107 108
-161.7028454 -164.8971500 -167.8659216 -171.7063434
109 110 111 112
-175.6495945 -179.5108370 -182.9547051 -187.9661067
113 114 115 116
-193.8319521 -198.9616599 -201.6350395 -204.3438350
117 118 119 120
-207.9176375 -211.1507140 -215.2380163 -218.6801288
121 122 123 124
-221.8621088 -224.6929477 -227.2231825 -230.3107054
125 126 127 128
-233.2961903 -236.8840042 -240.5034943 -244.2888099
129 130 131 132
-247.9140460 -251.9373797 -256.0073651 -260.4327199
133 134 135 136
-266.0526026 -271.4905104 -277.4029353 -283.2604732
137 138 139 140
-288.6895273 -293.2654954 -298.6399536 -304.4509655
141 142 143 144
-310.1260556 -315.5193516 -320.3536236 -324.4764174
145 146 147 148
-328.6879660 -332.7216340 -336.2992019 -339.8277912
149 150 151 152
-343.6155651 -347.3262003 -350.4477580 -353.1987654
153 154 155 156
-356.1250837 -359.7163519 -362.5959633 -365.6828492
157 158 159 160
-368.5623745 -371.8161007 -374.4125481 -377.2747733
161 162 163 164
-379.7049788 -382.2380197 -384.9842962 -388.1560308
165 166 167 168
-391.2678363 -394.2565381 -396.7983950 -399.0418318
169 170 171 172
-400.9365768 -403.1261222 -405.6987783 -408.0515457
173 174 175 176
-410.6783151 -412.8776559 -414.7690005 -416.2641724
177 178 179 180
-418.5796859 -420.4585454 -421.9333296 -423.2635020
181
-425.0572586 #upper bound CI
cumsum(tapply(all_data$all_post_data$GPP_treatment_q975 ,
round(all_data$all_post_data$Doy_water ),
function(x) mean(x,na.rm=T)))*12/1000000*1800*48 1 2 3 4
-0.9773301 -1.7365103 -2.3878065 -2.7640341
5 6 7 8
-2.7227005 -3.4101179 -4.4111840 -5.5583269
9 10 11 12
-7.1654173 -8.4983455 -9.4540580 -10.7568380
13 14 15 16
-11.2288151 -12.2292977 -13.1206277 -14.0631610
17 18 19 20
-14.1389150 -14.3652369 -15.5739079 -16.8910769
21 22 23 24
-17.9842423 -18.8776291 -19.7010002 -20.5477976
25 26 27 28
-21.6411106 -22.8572410 -24.1939702 -25.4146335
29 30 31 32
-26.6396369 -27.9126739 -29.1871161 -30.3560545
33 34 35 36
-31.5185941 -32.5824674 -33.5157878 -34.4908104
37 38 39 40
-35.2921918 -36.1091777 -36.8841326 -37.6540611
41 42 43 44
-38.1215724 -38.5804274 -38.5603566 -39.5419251
45 46 47 48
-39.8342308 -40.0778929 -40.1744216 -41.1372098
49 50 51 52
-42.2863499 -43.6891793 -44.7981032 -46.0369184
53 54 55 56
-47.5625953 -48.9108585 -49.3118606 -49.9402699
57 58 59 60
-50.8930728 -51.3288959 -52.3320939 -53.6736458
61 62 63 64
-54.4172631 -55.1698481 -55.1226951 -55.4545006
65 66 67 68
-55.5024789 -56.9522073 -58.1997283 -59.8207853
69 70 71 72
-60.9889892 -63.0823382 -64.9484184 -67.1164358
73 74 75 76
-68.3004095 -69.4188002 -71.1380464 -72.9473890
77 78 79 80
-74.4760707 -75.8791549 -77.3418599 -79.0699539
81 82 83 84
-80.3606382 -81.5585980 -82.3491779 -83.4893185
85 86 87 88
-84.0558106 -86.0617244 -87.9438891 -89.3022585
89 90 91 92
-89.7812267 -91.8090825 -93.4399883 -94.6697734
93 94 95 96
-95.9764794 -97.0232519 -98.5427449 -100.4815451
97 98 99 100
-102.0695474 -103.8050232 -105.6669098 -107.5488642
101 102 103 104
-109.3239329 -111.9441925 -113.2614185 -116.0425512
105 106 107 108
-118.4672587 -121.3888299 -123.5823453 -127.0827851
109 110 111 112
-130.5566711 -133.7018627 -135.9894172 -139.3795478
113 114 115 116
-142.9347263 -146.1586379 -148.2351155 -149.8410220
117 118 119 120
-152.5425718 -155.0811068 -158.0394922 -160.9246735
121 122 123 124
-163.9240088 -166.5795842 -168.9078838 -171.8697704
125 126 127 128
-174.7948341 -178.1248008 -181.4278160 -184.9336608
129 130 131 132
-188.3855166 -191.9547448 -194.5653266 -197.9361824
133 134 135 136
-202.9539171 -207.7603444 -213.0058322 -218.2271508
137 138 139 140
-222.9968941 -226.4809993 -230.7781715 -235.7867826
141 142 143 144
-240.5185989 -244.9356960 -249.0914903 -252.7047984
145 146 147 148
-256.3440353 -259.8134558 -262.7843776 -265.7105719
149 150 151 152
-268.8552036 -272.0272058 -274.8720019 -277.4709694
153 154 155 156
-280.3099023 -283.8962116 -286.8704282 -290.0636647
157 158 159 160
-293.2283222 -296.6235684 -299.5882151 -302.6724260
161 162 163 164
-305.5551562 -308.4986825 -311.5692616 -314.7529900
165 166 167 168
-317.8834524 -320.9905134 -323.6593471 -326.0462630
169 170 171 172
-328.2307247 -330.5861738 -333.1920290 -335.7536914
173 174 175 176
-338.4000043 -340.7926167 -342.6861411 -343.9849131
177 178 179 180
-346.3452623 -348.4353914 -350.3416947 -352.2076520
181
-354.3639067 all_data$all_pre_datawrite.csv(
all_data$all_pre_data,
paste0(out.path, Sys.Date(), "_all_pre_compost_data.csv"),
quote = T,
row.names = F
)
all_data$all_post_datawrite.csv(
all_data$all_pre_data,
paste0(out.path, Sys.Date(), "_all_post_compost_data.csv"),
quote = T,
row.names = F
)---
title: "Data visualizataions of GAM data Generated by Housen bootstrapping 500 times"
output: html_notebook
---


```{r echo = FALSE}
rm(list=ls())

require(dplyr)
require(mgcv)
library(mgcv)
library(ggplot2)
library(ISLR)
library(mgcv)
library(boot)
require(matrixStats)
library(zoo)
```


```{r}
na.count<-function(x) sum(is.na(x))
na.mean<-function(x) ifelse(is.nan(mean(x,na.rm=T)),NA,mean(x,na.rm=T))
```


#Defining the data I/O directory

```{r}
## change root.path as needed
root.path<-"C:\\Users\\tfens\\R_REPOS\\Flux_processing\\Concord_R_Code\\Concord_Post_Process\\"
# root.path<-"D:\\Housen\\Flux\\Data-exploring\\02_Concord_Eden\\"
#output folder
out.path<-paste0(root.path, "02_output\\01_GAM_output\\")

```



#load the RDA data produced in 12-Concord_bootstrap_GAMS_all
#Housen uploaded the data as rda files to google drive. 
```{r}

all_data <-  readRDS (file = "2022-04-20_all_data.rda" )

all_daily_data <- readRDS (file = "2022-04-20_all_data_daily.rda" )



all_filled_cum_matrix <- readRDS(file = "2022-04-20_all_filled_matrix.rda")

all_filled_matrix <- readRDS(file = "2022-04-20_all_filled_matrix.rda")

all_filled_matrix <- readRDS(file = "2022-04-20_all_predict_matrix.rda")

```


```{r}

all_data$all_pre_data
write.csv(
  all_data$all_pre_data,
  paste0(out.path, Sys.Date(), "_all_pre_compost_data.csv"),
  quote = T,
  row.names = F
)
```



#Figure for cumulative filled NEE by pre and post compost and group

```{r}

col.code2 <- list(col.name = c("control", "treatment"),
                 col = c("firebrick1", "deepskyblue"))

##### Figure for filled NEE
png(
    paste0(
      out.path,
      Sys.Date(),
      "_filled_cum_NEE_bygroups.png"
    ),
    width = 6.5,
    height = 3.5,
    units = "in",
    pointsize = 10,
    res = 400
  )
par(mar = c(4, 0.2, 0.5, 0.2), oma = c(0, 4, 0, 0.5), mfrow = c(1, 2))
plot(all_data[[1]]$TIMESTAMP,
     all_data[[1]]$NEE_filled_cum_control_mean,
     pch = 20,
     cex = 0.7,
     col = col.code2$col[1],
     las = 1,
     ylab = "",
     xlab = "",
     ylim = c(-100, 200))
mtext(side = 2,
      expression(Cumulative~CO[2]~flux~'('*g~C~m^{-2}*')'),
      outer = T,
      line = 2.5)
mtext(side = 1,
      "2019-2020",
      outer = F,
      line = 2.5)
points(all_data[[1]]$TIMESTAMP,
       all_data[[1]]$NEE_filled_cum_treatment_mean,
       pch = 20,
       cex = 0.7,
       col = col.code2$col[2])
lines(all_data[[1]]$TIMESTAMP,
      all_data[[1]]$NEE_filled_cum_control_q025,
      col = col.code2$col[1],
      lty = 2)
lines(all_data[[1]]$TIMESTAMP,
      all_data[[1]]$NEE_filled_cum_control_q975,
      col = col.code2$col[1],
      lty = 2)
lines(all_data[[1]]$TIMESTAMP,
      all_data[[1]]$NEE_filled_cum_treatment_q025,
      col = col.code2$col[2],
      lty = 2)
lines(all_data[[1]]$TIMESTAMP,
      all_data[[1]]$NEE_filled_cum_treatment_q975,
      col = col.code2$col[2],
      lty = 2)
legend("topleft",
       fill = col.code2$col,
       border = NA,
       legend = col.code2$col.name,
       ncol = 2,
       cex = 0.7,
       bty = "n")
abline(h = 0, col = "black")
plot(all_data[[2]]$TIMESTAMP,
     all_data[[2]]$NEE_filled_cum_control_mean,
     pch = 20,
     cex = 0.7,
     col = col.code2$col[1],
     las = 1,
     ylab = "",
     xlab = "",
     yaxt = "n",
     ylim = c(-100, 200))
mtext(side = 1,
      "2020-2021",
      outer = F,
      line = 2.5)
points(all_data[[2]]$TIMESTAMP,
       all_data[[2]]$NEE_filled_cum_treatment_mean,
       pch = 20,
       cex = 0.7,
       col = col.code2$col[2])
lines(all_data[[2]]$TIMESTAMP,
      all_data[[2]]$NEE_filled_cum_control_q025,
      col = col.code2$col[1],
      lty = 2)
lines(all_data[[2]]$TIMESTAMP,
      all_data[[2]]$NEE_filled_cum_control_q975,
      col = col.code2$col[1],
      lty = 2)
lines(all_data[[2]]$TIMESTAMP,
      all_data[[2]]$NEE_filled_cum_treatment_q025,
      col = col.code2$col[2],
      lty = 2)
lines(all_data[[2]]$TIMESTAMP,
      all_data[[2]]$NEE_filled_cum_treatment_q975,
      col = col.code2$col[2],
      lty = 2)
abline(h = 0, col = "black")
dev.off()
```
#figure for gap filled daily NEE. Updated to include the error range for the post compost control side daily data
```{r}
col.code4 <- list(col.name = c("control", "treatment"),
                  col = c("firebrick4", "deepskyblue4"),
                  col2 = c(rgb(1, 0, 0, 0.3), rgb(0, 0, 1, 0.3)),
                  col3 = c("lightcoral", "deepskyblue4"))

##### Figure for gap-filled daily NEE
png(
    paste0(
      out.path,
      Sys.Date(),
      "_filled_daily_NEE_bygroups.png"
    ),
    width = 6.5,
    height = 3.5,
    units = "in",
    pointsize = 10,
    res = 400
  )
par(mar = c(4, 0.2, 0.5, 0.2), oma = c(0, 4, 0, 0.5), mfrow = c(1, 2))
plot(0,
     0,
     las = 1,
     ylab = "",
     xlab = "",
     ylim = c(-10, 10),
     xlim = range(all_daily_data[[1]]$Doy_water),
     type = "n")
mtext(side = 1,
      "2019-2020",
      outer = F,
      line = 2.5)
mtext(side = 2,
      expression(CO[2]~flux~'('*g~C~m^{-2}~d^{-1}*')'),
      outer = T,
      line = 2.5)
polygon(c(all_daily_data[[1]]$Doy_water,
          rev(all_daily_data[[1]]$Doy_water)),
        c(all_daily_data[[1]]$NEE_filled_control_q975,
          rev(all_daily_data[[1]]$NEE_filled_control_q025)),
        col = col.code4$col3[1],
        border = NA)
polygon(c(all_daily_data[[1]]$Doy_water,
          rev(all_daily_data[[1]]$Doy_water)),
        c(all_daily_data[[1]]$NEE_filled_treatment_q975,
          rev(all_daily_data[[1]]$NEE_filled_treatment_q025)),
        col = col.code4$col2[2],
        border = NA)
points(all_daily_data[[1]]$Doy_water,
       all_daily_data[[1]]$NEE_filled_control_mean,
       pch = 20,
       cex = 0.7,
       col = col.code4$col[1])
points(all_daily_data[[1]]$Doy_water,
       all_daily_data[[1]]$NEE_filled_treatment_mean,
       pch = 20,
       cex = 0.7,
       col = col.code4$col[2])
legend("topleft",
       fill = col.code4$col,
       border = NA,
       legend = col.code2$col.name,
       ncol = 2,
       cex = 0.7,
       bty = "n")
plot(0,
     0,
     las = 1,
     ylab = "",
     xlab = "",
     yaxt = "n",
     ylim = c(-10, 10),
     xlim = range(all_daily_data[[2]]$Doy_water),
     type = "n")
mtext(side = 1,
      "2020-2021",
      outer = F,
      line = 2.5)
polygon(c(all_daily_data[[2]]$Doy_water,
          rev(all_daily_data[[2]]$Doy_water)),
        c(all_daily_data[[2]]$NEE_filled_control_q975,
          rev(all_daily_data[[2]]$NEE_filled_control_q025)),
        col = col.code4$col3[1],
        border = NA)
polygon(c(all_daily_data[[2]]$Doy_water,
          rev(all_daily_data[[2]]$Doy_water)),
        c(all_daily_data[[2]]$NEE_filled_treatment_q975,
          rev(all_daily_data[[2]]$NEE_filled_treatment_q025)),
        col = col.code4$col2[2],
        border = NA)
points(all_daily_data[[2]]$Doy_water,
       all_daily_data[[2]]$NEE_filled_control_mean,
       pch = 20,
       cex = 0.7,
       col = col.code4$col[1])
points(all_daily_data[[2]]$Doy_water,
       all_daily_data[[2]]$NEE_filled_treatment_mean,
       pch = 20,
       cex = 0.7,
       col = col.code4$col[2])
dev.off()

## save output
saveRDS(all_daily_data,
        paste0(out.path, Sys.Date(), "_all_daily_data.rda"))
```



#Tommy just getting a sense of the lists and data

```{r}
#Reco_mean and quantiles
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$RECO_predict_control_mean, ylim = c(0,35) )

par(new=TRUE)

plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$RECO_predict_control_q975, col = 'red', ylim = c(0,35))

par(new = TRUE)
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$RECO_predict_control_q025, col = 'blue', ylim = c(0,35))

#NEE
#1/2 hourly
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_control_mean)

#cumulative with error
plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_cum_control_q975, col='red', ylim = c(0,200)
     )

par(new=TRUE)

plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_cum_control_mean, col='grey', ylim = c(0,200))

par(new = TRUE)

plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_cum_control_q025 , col='red', ylim = c(0,200)
     )

```
#Creating a new data column for respiration 
#combination of Day Reco_predict_mean annd NEE_filled__means
#Our night time respiration data will be directly measured, while our daytime respiration data will be modeled. 

#This is actually unnneccesary. Just going to use NEE- Reco predict and then force all nightime GPP to zero. 

```{r}


####Control side pre-compost#########################
# creating new column called RECO_control_FIN
all_data$all_pre_data$RECO_control_FIN <- (all_data$all_pre_data$RECO_predict_control_mean * 1)




#assigning all night time respiration gap fill night NEE

all_data$all_pre_data$RECO_control_FIN[(all_data$all_pre_data$Rg <= 10 )]<-
  all_data$all_pre_data$NEE_filled_control_mean [(all_data$all_pre_data$Rg <= 10 )]

#plot and summary of recon control fin (observed nighttime NEE, and modeled Reco)
plot(all_data$all_pre_data$RECO_control_FIN )

summary(all_data$all_pre_data$RECO_control_FIN)

#plot and summary of just modeled Reco
plot(all_data$all_pre_data$RECO_predict_control_mean)
summary(all_data$all_pre_data$RECO_predict_control_mean)



#Thought I had to do all this below, but really do not need to. Can just use the filled NEE (see above). Filled NEE has actual NEE values for night time plus GAM filled. This ensures both actually measured values, excluding bad data and the best modeled nigh time NEE/ Reco

#Creating observed NEE variable to be used for nighttime reco

# all_data[[1]]$NEE_control_observed[all_data[[1]]$treatment== "control_pre_compost"] <- (all_data[[1]]$NEE[all_data[[1]]$treatment == "control_pre_compost"])
#
# plot(all_data$all_pre_data$NEE_control_observed) # around 1/2 night observed reco data is neg. maybe just use modeled data?
#
# #looking at how the new variable look
# summary(all_data$all_pre_data$NEE_control_observed)
# summary(all_data$all_pre_data$RECO_control_FIN)
#
# #assigning night time measured NEE values to the associated night time respiration values
#
# all_data$all_pre_data$RECO_control_FIN[all_data$all_pre_data$Rg <= 10 & !is.na( all_data$all_pre_data$NEE_control_observed)]<-
#   all_data$all_pre_data$NEE_control_observed[all_data$all_pre_data$Rg<=10  & !is.na( all_data$all_pre_data$NEE_control_observed)]
#
#
# #seeing how this shakes out
# plot(all_data$all_pre_data$RECO_control_FIN )
#
# summary(all_data$all_pre_data$RECO_control_FIN)
#
# summary(all_data$all_pre_data$RECO_predict_control_mean)
#
# summary(all_data$all_pre_data$NEE_filled_control_mean)
#
# plot(all_data$all_pre_data$TIMESTAMP, all_data$all_pre_data$RECO_control_FIN, col = 'red', ylim = c(-10, 30))
# par(new= TRUE)
# plot(all_data$all_pre_data$TIMESTAMP, all_data$all_pre_data$RECO_predict_control_mean, col = 'blue', ylim = c(-10, 30))

##############Control side post-compost######################
#creating new column called RECO_control_FIN
all_data$all_post_data$RECO_control_FIN <- (all_data$all_post_data$RECO_predict_control_mean * 1)

#assigning all night time respiration gap fill night NEE

all_data$all_post_data$RECO_control_FIN[(all_data$all_post_data$Rg <= 10 )]<-
  all_data$all_post_data$NEE_filled_control_mean [(all_data$all_post_data$Rg <= 10 )]

plot(all_data$all_post_data$RECO_control_FIN )

summary(all_data$all_post_data$RECO_control_FIN)

summary(all_data$all_post_data$NEE_filled_control_mean)


###############treatment side pre compost############################
all_data$all_pre_data$RECO_treatment_FIN <- (all_data$all_pre_data$RECO_predict_treatment_mean * 1)



###########treatment side post-compost###############
#creating new column called RECO_treatment_FIN
all_data$all_post_data$RECO_treatment_FIN <- (all_data$all_post_data$RECO_predict_treatment_mean * 1)


#assigning all night time respiration gap fill night NEE pre compost

all_data$all_pre_data$RECO_treatment_FIN[(all_data$all_pre_data$Rg <= 10 )]<-
  all_data$all_pre_data$NEE_filled_treatment_mean [(all_data$all_pre_data$Rg <= 10 )]

plot(all_data$all_pre_data$RECO_treatment_FIN )

summary(all_data$all_pre_data$RECO_treatment_FIN)

summary(all_data$all_pre_data$NEE_filled_treatment_mean)



#assigning all night time respiration gap fill night NEE post compost

all_data$all_post_data$RECO_treatment_FIN[(all_data$all_post_data$Rg <= 10 )]<-
  all_data$all_post_data$NEE_filled_treatment_mean [(all_data$all_post_data$Rg <= 10 )]

plot(all_data$all_post_data$RECO_treatment_FIN, ylim = c(0,5), col = "blue" )
par(new= TRUE)
plot(all_data$all_post_data$RECO_predict_treatment_mean, ylim = c(0,5), col = "red")

summary(all_data$all_post_data$RECO_treatment_FIN)
summary(all_data$all_post_data$RECO_predict_treatment_mean)

summary(all_data$all_post_data$NEE_filled_treatment_mean)

```


#creating 1/2 hour GPP control and treatment side values 
#sum(nee) = sum(gpp+reco)
#sum (nee-reco)= sum (gpp)
 
```{r}

######Creating 1/2 hourly GPP variable for post compost##########

#control post compost(Reco predict_control mean or Reco_control_fin)
#Use Reco_predict control mean and force all nighttime GPP to zero. 
all_data$all_post_data$GPP_control_mean <- (all_data$all_post_data$NEE_filled_control_mean - all_data$all_post_data$RECO_predict_control_mean)

summary(all_data$all_post_data$GPP_control_mean)

plot(all_data$all_post_data$GPP_control_mean)

#treatment post compost
all_data$all_post_data$GPP_treatment_mean<- (all_data$all_post_data$NEE_filled_treatment_mean - all_data$all_post_data$RECO_predict_treatment_mean  )

summary(all_data$all_post_data$GPP_treatment_mean)


plot(all_data$all_post_data$GPP_treatment_mean)

#######Make all night time GPP zero############ #Need to do this if using reco_predict!

#night time defined as Rg<= 10

#control post compost
all_data$all_post_data$GPP_control_mean [(all_data$all_post_data$Rg <= 10 )]<-0

plot(all_data$all_post_data$GPP_control_mean)

summary(all_data$all_post_data$GPP_control_mean)

#treatment  post compost
all_data$all_post_data$GPP_treatment_mean [(all_data$all_post_data$Rg <= 10 )]<-0

##########95% CI for GPP#####################

#making 95% confidence intervals for GPP control post compost
all_data$all_post_data$GPP_control_q025 <- (all_data$all_post_data$NEE_predict_control_q025 - all_data$all_post_data$RECO_predict_control_q025)


all_data$all_post_data$GPP_control_q975 <- (all_data$all_post_data$NEE_predict_control_q975 - all_data$all_post_data$RECO_predict_control_q975)

#making 95% confidence intervals for GPP treatment post compost
all_data$all_post_data$GPP_treatment_q025 <- (all_data$all_post_data$NEE_predict_treatment_q025 - all_data$all_post_data$RECO_predict_treatment_q025)


all_data$all_post_data$GPP_treatment_q975 <- (all_data$all_post_data$NEE_predict_treatment_q975 - all_data$all_post_data$RECO_predict_treatment_q975)

##########plotting 1/2hourly Reco and GPP############

#control post compost
plot(all_data$all_post_data$TIMESTAMP[(all_data$all_post_data$Rg > 10 )], all_data$all_post_data$GPP_control_mean[(all_data$all_post_data$Rg > 10 )], col='green', ylim = c(-12,6))


par(new=TRUE)

plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$RECO_predict_control_mean , col='red', ylim = c(-12,6)
     )
par(new=TRUE)

plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_control_mean , ylim = c(-12,6))


plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$GPP_control_mean, ylim = c(-12,6))

#treatment post compost
plot(all_data$all_post_data$TIMESTAMP[(all_data$all_post_data$Rg > 10 )], all_data$all_post_data$GPP_treatment_mean[(all_data$all_post_data$Rg > 10 )], col='green', ylim = c(-12,6))


par(new=TRUE)

plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$RECO_predict_treatment_mean , col='red', ylim = c(-12,6)
     )
par(new=TRUE)

plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_treatment_mean , ylim = c(-12,6))


plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$GPP_treatment_mean, ylim = c(-12,6))

```

#GPP pre compost variable

```{r}
######Creating 1/2 hourly GPP variable##########

#control pre compost
all_data$all_pre_data$GPP_control_mean <- (all_data$all_pre_data$NEE_filled_control_mean - all_data$all_pre_data$RECO_predict_control_mean)

summary(all_data$all_pre_data$GPP_control_mean)

#treatment pre compost
all_data$all_pre_data$GPP_treatment_mean<- (all_data$all_pre_data$NEE_filled_treatment_mean - all_data$all_pre_data$RECO_predict_treatment_mean  )

summary(all_data$all_pre_data$GPP_treatment_mean)

#######Make all night time GPP zero############ Need to do this since using Reco_predict_mean
#night time defined as Rg<= 10

# #control pre compost
all_data$all_pre_data$GPP_control_mean [(all_data$all_pre_data$Rg <= 10 )]<-0
 
# #treatment  pre compost
all_data$all_pre_data$GPP_treatment_mean [(all_data$all_pre_data$Rg <= 10 )]<-0

##########95% CI for GPP#####################

#making 95% confidence intervals for GPP control pre compost
all_data$all_pre_data$GPP_control_q025 <- (all_data$all_pre_data$NEE_predict_control_q025 - all_data$all_pre_data$RECO_predict_control_q025)


all_data$all_pre_data$GPP_control_q975 <- (all_data$all_pre_data$NEE_predict_control_q975 - all_data$all_pre_data$RECO_predict_control_q975)

#making 95% confidence intervals for GPP treatment pre compost
all_data$all_pre_data$GPP_treatment_q025 <- (all_data$all_pre_data$NEE_predict_treatment_q025 - all_data$all_pre_data$RECO_predict_treatment_q025)


all_data$all_pre_data$GPP_treatment_q975 <- (all_data$all_pre_data$NEE_predict_treatment_q975 - all_data$all_pre_data$RECO_predict_treatment_q975)
```






#Basic plots of control side post compost to make sure I'm on right track before making final figures
```{r}
#############Cumulative GPP, RECO , NEE control side post compost growing season#####



#cumulative respiration control side
plot(cumsum(tapply(all_data$all_post_data$RECO_predict_control_mean,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48,

     xaxt='n',
     xlab='Days of the growing season',
     ylab=expression(Cumulative~Reco~GPP~NEE~'('~g~C~m^{-2}~')'),
     main='Post Compost Control Side Cumulative RECO, GPP, NEE',
     cex.lab = 0.8,
     ylim = c(-800,800),
     lty=1,
     col="red",
     lwd=2,
     type="l")
axis(side=1,at=seq(0,360,by=30))

par(new = TRUE)
plot(cumsum(tapply(all_data$all_post_data$RECO_predict_control_q975 ,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48,

     xaxt='n',
     xlab='Days of the growing season',
     ylab=expression(Cumulative~Reco~GPP~NEE~'('~g~C~m^{-2}~')'),
     main='Post Compost Control Side Cumulative RECO, GPP, NEE',
     cex.lab = 0.8,
     ylim = c(-800,800),
     lty=2,
     col="red",
     lwd=2,
     type="l")
axis(side=1,at=seq(0,360,by=30))

par(new = TRUE)
plot(cumsum(tapply(all_data$all_post_data$RECO_predict_control_q025  ,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48,

     xaxt='n',
     xlab='Days of the growing season',
     ylab=expression(Cumulative~Reco~GPP~NEE~'('~g~C~m^{-2}~')'),
     main='Post Compost Control Side Cumulative RECO, GPP, NEE',
     cex.lab = 0.8,
     ylim = c(-800,800),
     lty=2,
     col="red",
     lwd=2,
     type="l")
axis(side=1,at=seq(0,360,by=30))

par(new = TRUE)
#cumulative GPP control side

plot(cumsum(tapply(all_data$all_post_data$GPP_control_mean,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
     xaxt='n',
     xlab='',
     ylab='',
     main='',
     ylim = c(-800,800),
     lty=1,
     col="green",
     lwd=2,
     type="l")
axis(side=1,at=seq(0,360,by=30))

par(new = TRUE)

plot(cumsum(tapply(all_data$all_post_data$GPP_control_q025,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
     xaxt='n',
     xlab='',
     ylab='',
     main='',
     ylim = c(-800,800),
     lty=2,
     col="green",
     lwd=2,
     type="l")
axis(side=1,at=seq(0,360,by=30))

par(new = TRUE)

plot(cumsum(tapply(all_data$all_post_data$GPP_control_q975 ,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
     xaxt='n',
     xlab='',
     ylab='',
     main='',
     ylim = c(-800,800),
     lty=2,
     col="green",
     lwd=2,
     type="l")
axis(side=1,at=seq(0,360,by=30))

par(new = TRUE)

plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_cum_control_mean, 
      xaxt='n',
     xlab='',
     ylab='',
     main='',
     ylim = c(-800,800),
     lty=1,
     col="grey",
     lwd=2,
     type="l")
     
axis(side=1,at=seq(0,360,by=30))

par(new =TRUE)
abline(h = 0, col = "black")

```

```{r}

#############Cumulative GPP, RECO , NEE treatmett side post compost growing season#####
#cumulative respiration treatment side
plot(cumsum(tapply(all_data$all_post_data$RECO_predict_treatment_mean,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48,

     xaxt='n',
     xlab='Days of the growing season',
     ylab=expression(Cumulative~Reco~GPP~NEE~'('~g~C~m^{-2}~')'),
     main='Post Compost Treatment Side Cumulative RECO, GPP, NEE',
     cex.lab = 0.8,
     ylim = c(-800,800),
     lty=1,
     col="red",
     lwd=2,
     type="l")
axis(side=1,at=seq(0,360,by=30))

par(new = TRUE)
plot(cumsum(tapply(all_data$all_post_data$RECO_predict_treatment_q975,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48,

     xaxt='n',
     xlab='Days of the growing season',
     ylab=expression(Cumulative~Reco~GPP~NEE~'('~g~C~m^{-2}~')'),
     main='Post Compost Treatment Side Cumulative RECO, GPP, NEE',
     cex.lab = 0.8,
     ylim = c(-800,800),
     lty=2,
     col="red",
     lwd=2,
     type="l")
axis(side=1,at=seq(0,360,by=30))

par(new = TRUE)
plot(cumsum(tapply(all_data$all_post_data$RECO_predict_treatment_q025,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48,

     xaxt='n',
     xlab='Days of the growing season',
     ylab=expression(Cumulative~Reco~GPP~NEE~'('~g~C~m^{-2}~')'),
     main='Post Compost Treatment Side Cumulative RECO, GPP, NEE',
     cex.lab = 0.8,
     ylim = c(-800,800),
     lty=2,
     col="red",
     lwd=2,
     type="l")
axis(side=1,at=seq(0,360,by=30))

par(new = TRUE)
#cumulative GPP treatment side

plot(cumsum(tapply(all_data$all_post_data$GPP_treatment_mean,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
     xaxt='n',
     xlab='',
     ylab='',
     main='',
     ylim = c(-800,800),
     lty=1,
     col="green",
     lwd=2,
     type="l")
axis(side=1,at=seq(0,360,by=30))

par(new = TRUE)

plot(cumsum(tapply(all_data$all_post_data$GPP_treatment_q025 ,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
     xaxt='n',
     xlab='',
     ylab='',
     main='',
     ylim = c(-800,800),
     lty=2,
     col="green",
     lwd=2,
     type="l")
axis(side=1,at=seq(0,360,by=30))

par(new = TRUE)

plot(cumsum(tapply(all_data$all_post_data$GPP_treatment_q975,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48,
     xaxt='n',
     xlab='',
     ylab='',
     main='',
     ylim = c(-800,800),
     lty=2,
     col="green",
     lwd=2,
     type="l")
axis(side=1,at=seq(0,360,by=30))

par(new = TRUE)

#cumulative NEE treatment side

plot(all_data$all_post_data$TIMESTAMP, all_data$all_post_data$NEE_filled_cum_treatment_mean, 
      xaxt='n',
     xlab='',
     ylab='',
     main='',
     ylim = c(-800,800),
     lty=1,
     col="grey",
     lwd=2,
     type="l")
     
axis(side=1,at=seq(0,360,by=30))

par(new =TRUE)
abline(h = 0, col = "black")
```



```{r}
#blank
all_data$all_post_data$blank = (all_data$all_post_data$RECO_control_FIN *
                                      0)

all_data$all_post_data$blank[(all_data$all_post_data$blank == 0)] <-
  NA
```


#Plotting NEE variables###

####panel 1 half-hourly non-gap filled and half hourly gap filled###
#panel 1/2 hourly GPP_treatment_mean and Respiration
#panel 3 cumulative sums of NEE, Respiration and neg GPP


#Treatment side 1/2 hourly
```{r}
summary (all_data$all_pre_data$NEE)

summary(all_data$all_post_data$NEE)

head(all_data$all_pre_data$TIMESTAMP)

nrow(all_data$all_pre_data$NEE_predict_control_mean
        )
```



```{r}



col.code3 <- list(col.name = c("control (modeled)", "treatment (modeled)",
                               "control (observed)", "treatment (observed)"),
                  col = c("firebrick1", "deepskyblue", "firebrick4", "deepskyblue4"))

#figure for cumulative and filled NEE and Reco

target.plot.var_nee <- c("NEE_filled_treatment_mean",
                         "GPP_treatment_mean",
                         "blank")

target.plot.var_nee.title <- c(
  expression(FC ~ '(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
  expression(GPP ~ ';' ~ Reco~'(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
  expression(Cumulative~sum~ '(' ~ g ~ C ~ m ^ {-2 } ~ ')')
)

## locate the start of each month
month.loc <- which(
  all_data$all_post_data$TIMESTAMP$mday == 1 &
    all_data$all_post_data$TIMESTAMP$hour == 0 &
    all_data$all_post_data$TIMESTAMP$min == 0
)
month.ticks <-
  substr(seq(
    all_data$all_post_data$TIMESTAMP[month.loc[1]],
    all_data$all_post_data$TIMESTAMP[month.loc[length(month.loc)]],
    by = "months"
  ), 6, 7)

## daily average values
daily_nee.tmp <-
  data.frame(
    date = tapply(
      all_data$all_post_data$time.id,
      all_data$all_post_data$Doy_water,
      min
    ),
    daily_nee = tapply(
      all_data$all_post_data$NEE_filled_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp = tapply(
      all_data$all_post_data$GPP_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco = tapply(
      all_data$all_post_data$RECO_predict_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    )
  )


## convert NEE, RECO_predict_treatment_mean gpp to cumulative sum of carbon
# convert to cumulative carbon
for(ll in 2:4) {
  # convert to daily units
  daily_nee.tmp[, ll] <-
    daily_nee.tmp[, ll] * 12 / 1000000 * 1800 * 48
  daily_nee.tmp[is.na(daily_nee.tmp[, ll]), ll] <- 0
  
  # calculate cumulative sum, hard-coded with first/second years
  daily_nee.tmp[1:365, ll] <-
    cumsum(daily_nee.tmp[1:365, ll])
  daily_nee.tmp[366:nrow(daily_nee.tmp), ll] <-
    cumsum(daily_nee.tmp[366:nrow(daily_nee.tmp), ll])
  
  # set break (missing value) between two years
  daily_nee.tmp[366, ll] <- NA
}

## begin plot   
png(
  paste0(out.path, "NEE_treatment_growing_post_compost_concord",
    all_data$all_post_data$TIMESTAMP$year[1] + 1900, "_",
    all_data$all_post_data$TIMESTAMP$yday[1] + 1, "_",
    all_data$all_post_data$TIMESTAMP$year[nrow(all_data$all_post_data)] + 1900, "_",
    all_data$all_post_data$TIMESTAMP$yday[nrow(all_data$all_post_data)] + 1, "_",
    "NEE_",
    Sys.Date(), ".png"
  ),
  width = 5.5,
  height = 6,
  units = "in",
  res = 300,
  pointsize = 11,
  bg = "white"
)
par(oma = c(4, 4.5, 0.5, 0.5),
    mar = c(0, 0, 0.25, 0))

par(fig = c(0, 1, 2 / 3, 1), new = FALSE)
plot(
  all_data[[2]]$TIMESTAMP,
  all_data[[2]]$NEE_filled_treatment_mean,
  pch = 20,
  cex = 0.7,
  col = col.code3$col[1],
  las = 1,
  ylab = "",
  xlab = "",
  xaxt = "n",
  xaxs = "i",
  #yaxt = "n",
  ylim = c(-20, 20),
  cex.axis = 0.8
)

points(
  all_data[[2]]$TIMESTAMP[all_data[[2]]$treatment == "treatment_post_compost"],
  all_data[[2]]$NEE[all_data[[2]]$treatment == "treatment_post_compost"],
  pch = 20,
  cex = 0.7,
  col = col.code3$col[4]
)

abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")

text(
  x = all_data$all_post_data$time.id[1],
  y = 30,
  paste0("(a)"),
  adj = c(0, 1),
  cex = 0.9,
  
)

mtext(
  side = 2,
  target.plot.var_nee.title[[1]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)

## panel b
par(fig = c(0, 1, 1 / 3, 2 / 3), new = TRUE)
plot(
  all_data$all_post_data$time.id,
  all_data$all_post_data$GPP_treatment_mean,
  xlab = "",
  ylab = "",
  cex = 0.5,
  col = "forestgreen",
  bg = "forestgreen",
  xaxt = "n",
  las = 1,
  pch = 21,
  xaxs = "i",
  ylim = c(-10, 10),
  cex.axis = 0.8
)

points(
  all_data$all_post_data$time.id,
  all_data$all_post_data$RECO_predict_treatment_mean,
  cex = 0.5,
  col = "red",
  bg = "red",
  pch = 21,
)

abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")

text(
  x = all_data$all_post_data$time.id[1],
  y = 30,
  paste0("(b)"),
  adj = c(0, 1),
  cex = 0.9
)

mtext(
  side = 2,
  target.plot.var_nee.title[[2]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)


## panel c
par(fig = c(0, 1, 0, 1 / 3), new = TRUE)
plot(
  daily_nee.tmp$date,
  daily_nee.tmp$daily_nee,
  type = "l",
  lwd = 1.5,
  col = "black",
  xlab = "",
  ylab = "",
  xaxt = "n",
  las = 1,
  xaxs = "i",
  ylim = c(-375, 375),
  cex.axis = 0.8
)

lines(daily_nee.tmp$date,
      daily_nee.tmp$daily_gpp,
      lwd = 1.5,
      col = "forestgreen",
      lty = 2
      )

lines(daily_nee.tmp$date,
      daily_nee.tmp$daily_reco,
      lwd = 1.5,
      col = "red",
      lty = 3
      )

abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")

axis(
  side = 1,
  at = all_data$all_post_data$time.id[month.loc],
  labels = month.ticks,
  tck = -.025,
  cex.axis = 0.8
)

text(
  x = all_data$all_post_data$time.id[1],
  y = 1200,
  paste0("(c)"),
  adj = c(0, 1),
  cex = 0.9
)

mtext(
  side = 2,
  target.plot.var_nee.title[[3]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)

axis(
  side = 1,
  at = c(2019.75, 2020.5, 2021.17),
  label = c(2019, 2020, 2021),
  cex.axis = 0.8,
  tck = -.025,
  lty = 0,
  bty = "n",
  line = 0.9
)

mtext(
  side = 1,
  "Month / Year",
  line = 3,
  outer = FALSE,
  cex = 0.8
)


dev.off()
```



####panel 1 half-hourly non-gap filled and half hourly gap filled###
#panel 2 daily GPP_treatment_mean and Respiration with error lines shaded
#panel 3 cumulative sums of NEE, Respiration and neg GPP with error lines


#Treatment side Daily



```{r}



col.code4 <- list(col.name = c( "treatment (modeled)","treatment (observed)"),
                               
                  col = c("deepskyblue", "deepskyblue4"))

col.code5 <- list(col.name = c("Reco", "GPP"),
                
                  col3 = c("lightcoral", "lightgreen"))




#figure for cumulative and filled NEE and Reco

target.plot.var_nee <- c("NEE_filled_treatment_mean",
                         "GPP_treatment_mean",
                         "blank")

target.plot.var_nee.title <- c(
  expression(FC ~ '(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
  expression(GPP ~ ';' ~ Reco~'(' ~ mu ~ mol ~ m ^ {-2 } ~ d ^ { -1 } ~ ')'),
  expression(Cumulative~sum~ '(' ~ g ~ C ~ m ^ {-2 } ~ ')')
)

## locate the start of each month
month.loc <- which(
  all_data$all_post_data$TIMESTAMP$mday == 1 &
    all_data$all_post_data$TIMESTAMP$hour == 0 &
    all_data$all_post_data$TIMESTAMP$min == 0
)
month.ticks <-
  substr(seq(
    all_data$all_post_data$TIMESTAMP[month.loc[1]],
    all_data$all_post_data$TIMESTAMP[month.loc[length(month.loc)]],
    by = "months"
  ), 6, 7)

## daily average values
daily_nee.tmp <-
  data.frame(
    date = tapply(
      all_data$all_post_data$time.id,
      all_data$all_post_data$Doy_water,
      min
    ),
    daily_nee = tapply(
      all_data$all_post_data$NEE_filled_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp = tapply(
      all_data$all_post_data$GPP_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco = tapply(
      all_data$all_post_data$RECO_predict_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp_q025 = tapply(
      all_data$all_post_data$GPP_treatment_q025,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gppq975 = tapply(
      all_data$all_post_data$GPP_treatment_q975,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco025 = tapply(
      all_data$all_post_data$RECO_predict_treatment_q025,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco975 = tapply(
      all_data$all_post_data$RECO_predict_treatment_q975,
      all_data$all_post_data$Doy_water,
      na.mean
    )
  )

#Creating a second daily value data frame that is not cum_sum for panel B
daily_nee.tmp_2 <-
  data.frame(
    date = tapply(
      all_data$all_post_data$time.id,
      all_data$all_post_data$Doy_water,
      min
    ),
    daily_nee = tapply(
      all_data$all_post_data$NEE_filled_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp = tapply(
      all_data$all_post_data$GPP_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco = tapply(
      all_data$all_post_data$RECO_predict_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp_q025 = tapply(
      all_data$all_post_data$GPP_treatment_q025,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gppq975 = tapply(
      all_data$all_post_data$GPP_treatment_q975,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco025 = tapply(
      all_data$all_post_data$RECO_predict_treatment_q025,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco975 = tapply(
      all_data$all_post_data$RECO_predict_treatment_q975,
      all_data$all_post_data$Doy_water,
      na.mean
    )
  )

## convert NEE, RECO_predict_treatment_mean gpp to cumulative sum of carbon
# convert to cumulative carbon
for(ll in 2:8) {
  # convert to daily units
  daily_nee.tmp[, ll] <-
    daily_nee.tmp[, ll] * 12 / 1000000 * 1800 * 48
  daily_nee.tmp[is.na(daily_nee.tmp[, ll]), ll] <- 0
  
  # calculate cumulative sum, hard-coded with first/second years
  daily_nee.tmp[1:365, ll] <-
    cumsum(daily_nee.tmp[1:365, ll])
  daily_nee.tmp[366:nrow(daily_nee.tmp), ll] <-
    cumsum(daily_nee.tmp[366:nrow(daily_nee.tmp), ll])
  
  # set break (missing value) between two years
  daily_nee.tmp[366, ll] <- NA
}

## begin plot   
png(
  paste0(out.path, "NEE_treatment_Daily_growing_post_compost_concord",
    all_data$all_post_data$TIMESTAMP$year[1] + 1900, "_",
    all_data$all_post_data$TIMESTAMP$yday[1] + 1, "_",
    all_data$all_post_data$TIMESTAMP$year[nrow(all_data$all_post_data)] + 1900, "_",
    all_data$all_post_data$TIMESTAMP$yday[nrow(all_data$all_post_data)] + 1, "_",
    "NEE_",
    Sys.Date(), ".png"
  ),
  width = 5.5,
  height = 6,
  units = "in",
  res = 300,
  pointsize = 11,
  bg = "white"
)
par(oma = c(4, 4.5, 0.5, 0.5),
    mar = c(0, 0, 0.25, 0))

par(fig = c(0, 1, 2 / 3, 1), new = FALSE)
plot(
  all_data[[2]]$TIMESTAMP,
  all_data[[2]]$NEE_filled_treatment_mean,
  pch = 20,
  cex = 0.7,
  col = col.code4$col[1],
  las = 1,
  ylab = "",
  xlab = "",
  xaxt = "n",
  xaxs = "i",
  #yaxt = "n",
  ylim = c(-15, 15),
  cex.axis = 0.8
)

points(
  all_data[[2]]$TIMESTAMP[all_data[[2]]$treatment == "treatment_post_compost"],
  all_data[[2]]$NEE[all_data[[2]]$treatment == "treatment_post_compost"],
  pch = 20,
  cex = 0.7,
  col = col.code4$col[2]
)

abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")

text(
  x = all_data$all_post_data$time.id[1],
  y = 30,
  paste0("(a)"),
  adj = c(0, 1),
  cex = 0.9,
  
)

mtext(
  side = 2,
  target.plot.var_nee.title[[1]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)
legend(
  "topleft",
  fill = col.code4$col,
  border = NA,
  legend = col.code4$col.name,
  ncol = 2,
  cex = 0.7,
  bty = "n"
)

## panel b
par(fig = c(0, 1, 1 / 3, 2 / 3), new = TRUE)
plot(
  daily_nee.tmp_2$date,
  daily_nee.tmp_2$daily_gpp,
  xlab = "",
  ylab = "",
  cex = 0,
  col = "forestgreen",
  bg = "forestgreen",
  xaxt = "n",
  las = 1,
  pch = 21,
  xaxs = "i",
  ylim = c(-10, 10),
  cex.axis = 0.8
)
polygon(c(daily_nee.tmp_2$date,
          rev(daily_nee.tmp_2$date)),
        c(daily_nee.tmp_2$daily_reco975 ,
          rev(daily_nee.tmp_2$daily_reco025)),
        col = col.code5$col3[1],
        border = NA)
polygon(c(daily_nee.tmp_2$date,
          rev(daily_nee.tmp_2$date)),
        c(daily_nee.tmp_2$daily_gppq975 ,
          rev(daily_nee.tmp_2$daily_gpp_q025)),
        col = col.code5$col3[2],
        border = NA)
points(
  daily_nee.tmp_2$date,
  daily_nee.tmp_2$daily_reco,
  cex = 0.6,
  col = "red",
  bg = "red",
  pch = 21,
)

points(
  daily_nee.tmp_2$date,
  daily_nee.tmp_2$daily_gpp,
  cex = 0.6,
  col = "forestgreen",
  bg = "forestgreen",
  pch = 21,
)


abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")

text(
  x = all_data$all_post_data$time.id[1],
  y = 30,
  paste0("(b)"),
  adj = c(0, 1),
  cex = 0.9
)

mtext(
  side = 2,
  target.plot.var_nee.title[[2]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)


## panel c
par(fig = c(0, 1, 0, 1 / 3), new = TRUE)
plot(
  daily_nee.tmp$date,
  daily_nee.tmp$daily_nee,
  type = "l",
  lwd = 1.5,
  col = "black",
  xlab = "",
  ylab = "",
  xaxt = "n",
  las = 1,
  xaxs = "i",
  ylim = c(-600, 800),
  cex.axis = 0.8
)

lines(daily_nee.tmp$date,
      daily_nee.tmp$daily_gpp,
      lwd = 1.5,
      col = "forestgreen",
      lty = 1
      )

lines(daily_nee.tmp$date,
      daily_nee.tmp$daily_reco,
      lwd = 1.5,
      col = "red",
      lty = 1
      )

lines(daily_nee.tmp$date,
      daily_nee.tmp$daily_gpp_q025 ,
      lwd = 1,
      col = "forestgreen",
      lty = 3
      )

lines(daily_nee.tmp$date,
      daily_nee.tmp$daily_gppq975  ,
      lwd = 1,
      col = "forestgreen",
      lty = 3
      )

lines(daily_nee.tmp$date,
      daily_nee.tmp$daily_reco025,
      lwd = 1,
      col = "red",
      lty = 3
      )
lines(daily_nee.tmp$date,
      daily_nee.tmp$daily_reco975,
      lwd = 1,
      col = "red",
      lty = 3
      )

abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")

axis(
  side = 1,
  at = all_data$all_post_data$time.id[month.loc],
  labels = month.ticks,
  tck = -.025,
  cex.axis = 0.8
)

text(
  x = all_data$all_post_data$time.id[1],
  y = 1200,
  paste0("(c)"),
  adj = c(0, 1),
  cex = 0.9
)

mtext(
  side = 2,
  target.plot.var_nee.title[[3]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)

axis(
  side = 1,
  at = c(2019.75, 2020.92, 2021.17),
  label = c(2019, 2020, 2021),
  cex.axis = 0.8,
  tck = -.025,
  lty = 0,
  bty = "n",
  line = 0.9
)

mtext(
  side = 1,
  "Month / Year",
  line = 3,
  outer = FALSE,
  cex = 0.8
)


dev.off()

```
#Appendix figure showing observed and modeled  treatment side post compost application

####panel 1 half-hourly non-gap filled and half hourly gap filled###
#panel 2 daily GPP_treatment_mean and Respiration with error lines shaded



```{r}
col.code4 <- list(col.name = c( "treatment (modeled)","treatment (observed)"),
                               
                  col = c("deepskyblue", "deepskyblue4"))

col.code5 <- list(col.name = c("Reco", "GPP"),
                
                  col3 = c("lightcoral", "lightgreen"))




#figure for cumulative and filled NEE and Reco

target.plot.var_nee <- c("NEE_filled_treatment_mean",
                         "GPP_treatment_mean",
                         "blank")

target.plot.var_nee.title <- c(
  expression(FC ~ '(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
  expression(GPP ~ ';' ~ Reco~'(' ~ mu ~ mol ~ m ^ {-2 } ~ d ^ { -1 } ~ ')'),
  expression(Cumulative~sum~ '(' ~ g ~ C ~ m ^ {-2 } ~ ')')
)

## locate the start of each month
month.loc <- which(
  all_data$all_post_data$TIMESTAMP$mday == 1 &
    all_data$all_post_data$TIMESTAMP$hour == 0 &
    all_data$all_post_data$TIMESTAMP$min == 0
)
month.ticks <-
  substr(seq(
    all_data$all_post_data$TIMESTAMP[month.loc[1]],
    all_data$all_post_data$TIMESTAMP[month.loc[length(month.loc)]],
    by = "months"
  ), 6, 7)

## daily average values
daily_nee.tmp <-
  data.frame(
    date = tapply(
      all_data$all_post_data$time.id,
      all_data$all_post_data$Doy_water,
      min
    ),
    daily_nee = tapply(
      all_data$all_post_data$NEE_filled_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp = tapply(
      all_data$all_post_data$GPP_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco = tapply(
      all_data$all_post_data$RECO_predict_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp_q025 = tapply(
      all_data$all_post_data$GPP_treatment_q025,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gppq975 = tapply(
      all_data$all_post_data$GPP_treatment_q975,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco025 = tapply(
      all_data$all_post_data$RECO_predict_treatment_q025,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco975 = tapply(
      all_data$all_post_data$RECO_predict_treatment_q975,
      all_data$all_post_data$Doy_water,
      na.mean
    )
  )

#Creating a second daily value data frame that is not cum_sum for panel B
daily_nee.tmp_2 <-
  data.frame(
    date = tapply(
      all_data$all_post_data$time.id,
      all_data$all_post_data$Doy_water,
      min
    ),
    daily_nee = tapply(
      all_data$all_post_data$NEE_filled_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp = tapply(
      all_data$all_post_data$GPP_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco = tapply(
      all_data$all_post_data$RECO_predict_treatment_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp_q025 = tapply(
      all_data$all_post_data$GPP_treatment_q025,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gppq975 = tapply(
      all_data$all_post_data$GPP_treatment_q975,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco025 = tapply(
      all_data$all_post_data$RECO_predict_treatment_q025,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco975 = tapply(
      all_data$all_post_data$RECO_predict_treatment_q975,
      all_data$all_post_data$Doy_water,
      na.mean
    )
  )

## convert NEE, RECO_predict_treatment_mean gpp to cumulative sum of carbon
# convert to cumulative carbon
for(ll in 2:8) {
  # convert to daily units
  daily_nee.tmp[, ll] <-
    daily_nee.tmp[, ll] * 12 / 1000000 * 1800 * 48
  daily_nee.tmp[is.na(daily_nee.tmp[, ll]), ll] <- 0
  
  # calculate cumulative sum, hard-coded with first/second years
  daily_nee.tmp[1:365, ll] <-
    cumsum(daily_nee.tmp[1:365, ll])
  daily_nee.tmp[366:nrow(daily_nee.tmp), ll] <-
    cumsum(daily_nee.tmp[366:nrow(daily_nee.tmp), ll])
  
  # set break (missing value) between two years
  daily_nee.tmp[366, ll] <- NA
}

## begin plot   
png(
  paste0(out.path, "NEE_treatment_appendix_model_observed_daily",
    all_data$all_post_data$TIMESTAMP$year[1] + 1900, "_",
    all_data$all_post_data$TIMESTAMP$yday[1] + 1, "_",
    all_data$all_post_data$TIMESTAMP$year[nrow(all_data$all_post_data)] + 1900, "_",
    all_data$all_post_data$TIMESTAMP$yday[nrow(all_data$all_post_data)] + 1, "_",
    "NEE_",
    Sys.Date(), ".png"
  ),
  width = 5.5,
  height = 6,
  units = "in",
  res = 300,
  pointsize = 11,
  bg = "white"
)
par(oma = c(4, 4.5, 0.5, 0.5),
    mar = c(0, 0, 0.25, 0))

par(fig = c(0, 1, 1 /2, 1), new = FALSE)
plot(
  all_data[[2]]$TIMESTAMP,
  all_data[[2]]$NEE_filled_treatment_mean,
  pch = 20,
  cex = 0.7,
  col = col.code4$col[1],
  las = 1,
  ylab = "",
  xlab = "",
  xaxt = "n",
  xaxs = "i",
  #yaxt = "n",
  ylim = c(-15, 15),
  cex.axis = 0.8
)

points(
  all_data[[2]]$TIMESTAMP[all_data[[2]]$treatment == "treatment_post_compost"],
  all_data[[2]]$NEE[all_data[[2]]$treatment == "treatment_post_compost"],
  pch = 20,
  cex = 0.7,
  col = col.code4$col[2]
)

abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")

text(
  x = all_data$all_post_data$time.id[1],
  y = 30,
  paste0("(a)"),
  adj = c(0, 1),
  cex = 0.9,
  
)



#adding panel label
mtext(
  "A", side=2, line=3, at=14, las = 1
  )

mtext(
  side = 2,
  target.plot.var_nee.title[[1]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)

legend(
  "topleft",
  fill = col.code4$col,
  border = NA,
  legend = col.code4$col.name,
  ncol = 2,
  cex = 0.7,
  bty = "n"
)

## panel b
par(fig = c(0, 1, 0, 1 / 2), new = TRUE)
plot(
  daily_nee.tmp_2$date,
  daily_nee.tmp_2$daily_gpp,
  xlab = "",
  ylab = "",
  cex = 0,
  col = "forestgreen",
  bg = "forestgreen",
  xaxt = "n",
  las = 1,
  pch = 21,
  xaxs = "i",
  ylim = c(-10, 10),
  cex.axis = 0.8
)
polygon(c(daily_nee.tmp_2$date,
          rev(daily_nee.tmp_2$date)),
        c(daily_nee.tmp_2$daily_reco975 ,
          rev(daily_nee.tmp_2$daily_reco025)),
        col = col.code5$col3[1],
        border = NA)
polygon(c(daily_nee.tmp_2$date,
          rev(daily_nee.tmp_2$date)),
        c(daily_nee.tmp_2$daily_gppq975 ,
          rev(daily_nee.tmp_2$daily_gpp_q025)),
        col = col.code5$col3[2],
        border = NA)
points(
  daily_nee.tmp_2$date,
  daily_nee.tmp_2$daily_reco,
  cex = 0.6,
  col = "red",
  bg = "red",
  pch = 21,
)

points(
  daily_nee.tmp_2$date,
  daily_nee.tmp_2$daily_gpp,
  cex = 0.6,
  col = "forestgreen",
  bg = "forestgreen",
  pch = 21,
)


abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp$date[366], lwd= 1.5, col = "black")

text(
  x = all_data$all_post_data$time.id[1],
  y = 30,
  paste0("(b)"),
  adj = c(0, 1),
  cex = 0.9
)

mtext(
  side = 2,
  target.plot.var_nee.title[[2]],
  line = 3,
  outer = FALSE,
  cex = 0.8
  
  
)

axis(
  side = 1,
  at = all_data$all_post_data$time.id[month.loc],
  labels = month.ticks,
  tck = -.025,
  cex.axis = 0.8
)

text(
  x = all_data$all_post_data$time.id[1],
  y = 1200,
  paste0("(c)"),
  adj = c(0, 1),
  cex = 0.9
)


axis(
  side = 1,
  at = c(2019.75, 2020.92, 2021.17),
  label = c(2019, 2020, 2021),
  cex.axis = 0.8,
  tck = -.025,
  lty = 0,
  bty = "n",
  line = 0.9
)

mtext(
  side = 1,
  "Month / Year",
  line = 3,
  outer = FALSE,
  cex = 0.8
)
mtext(
  "B", side=2, line=3, at=9, las = 1
  )



dev.off()
```


####panel 1 Control half-hourly non-gap filled and half hourly gap filled###
#panel 2 1/2 hourly GPP_control_mean and Respiration with error lines shaded
#panel 3 cumulative sums of Control NEE, Respiration and GPP


#Control side side with 1/2 hourly data in panel 2



```{r}


#figure for cumulative and filled NEE and Reco

target.plot.var_nee_cont <- c("NEE_filled_control_mean",
                         "GPP_control_mean",
                         "blank")

target.plot.var_nee_cont.title <- c(
  expression(FC ~ '(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
  expression(GPP ~ ';' ~ Reco~'(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
  expression(Cumulative~sum~ '(' ~ g ~ C ~ m ^ {-2 } ~ ')')
)

## locate the start of each month
month.loc <- which(
  all_data$all_post_data$TIMESTAMP$mday == 1 &
    all_data$all_post_data$TIMESTAMP$hour == 0 &
    all_data$all_post_data$TIMESTAMP$min == 0
)
month.ticks <-
  substr(seq(
    all_data$all_post_data$TIMESTAMP[month.loc[1]],
    all_data$all_post_data$TIMESTAMP[month.loc[length(month.loc)]],
    by = "months"
  ), 6, 7)

## daily average values
daily_nee.tmp_cont <-
  data.frame(
    date = tapply(
      all_data$all_post_data$time.id,
      all_data$all_post_data$Doy_water,
      min
    ),
    daily_nee = tapply(
      all_data$all_post_data$NEE_filled_control_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp = tapply(
      all_data$all_post_data$GPP_control_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco = tapply(
      all_data$all_post_data$RECO_predict_control_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    )
  )

## convert NEE, RECO_predict_treatment_mean gpp to cumulative sum of carbon
# convert to cumulative carbon
for(ll in 2:4) {
  # convert to daily units
  daily_nee.tmp_cont[, ll] <-
    daily_nee.tmp_cont[, ll] * 12 / 1000000 * 1800 * 48
  daily_nee.tmp_cont[is.na(daily_nee.tmp_cont[, ll]), ll] <- 0
  
  # calculate cumulative sum, hard-coded with first/second years
  daily_nee.tmp_cont[1:365, ll] <-
    cumsum(daily_nee.tmp_cont[1:365, ll])
  daily_nee.tmp_cont[366:nrow(daily_nee.tmp_cont), ll] <-
    cumsum(daily_nee.tmp_cont[366:nrow(daily_nee.tmp_cont), ll])
  
  # set break (missing value) between two years
  daily_nee.tmp_cont[366, ll] <- NA
}

## begin plot   
png(
  paste0(out.path, "NEE_CONTROL_growing_post_compost_concord",
    all_data$all_post_data$TIMESTAMP$year[1] + 1900, "_",
    all_data$all_post_data$TIMESTAMP$yday[1] + 1, "_",
    all_data$all_post_data$TIMESTAMP$year[nrow(all_data$all_post_data)] + 1900, "_",
    all_data$all_post_data$TIMESTAMP$yday[nrow(all_data$all_post_data)] + 1, "_",
    "NEE_",
    Sys.Date(), ".png"
  ),
  width = 5.5,
  height = 6,
  units = "in",
  res = 300,
  pointsize = 11,
  bg = "white"
)
par(oma = c(4, 4.5, 0.5, 0.5),
    mar = c(0, 0, 0.25, 0))

par(fig = c(0, 1, 2 / 3, 1), new = FALSE)
plot(
  all_data[[2]]$TIMESTAMP,
  all_data[[2]]$NEE_filled_control_mean,
  pch = 20,
  cex = 0.7,
  col = col.code4$col[1],
  las = 1,
  ylab = "",
  xlab = "",
  xaxt = "n",
  xaxs = "i",
  #yaxt = "n",
  ylim = c(-20, 20),
  cex.axis = 0.8
)

points(
  all_data[[2]]$TIMESTAMP[all_data[[2]]$treatment == "control_post_compost"],
  all_data[[2]]$NEE[all_data[[2]]$treatment == "control_post_compost"],
  pch = 20,
  cex = 0.7,
  col = col.code4$col[4]
)

abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp_cont$date[366], lwd= 1.5, col = "black")

text(
  x = all_data$all_post_data$time.id[1],
  y = 30,
  paste0("(a)"),
  adj = c(0, 1),
  cex = 0.9
)

mtext(
  side = 2,
  target.plot.var_nee_cont.title[[1]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)

## panel b
par(fig = c(0, 1, 1 / 3, 2 / 3), new = TRUE)
plot(
  all_data$all_post_data$time.id,
  all_data$all_post_data$GPP_control_mean,
  xlab = "",
  ylab = "",
  cex = 0.5,
  col = "forestgreen",
  bg = "forestgreen",
  xaxt = "n",
  las = 1,
  pch = 21,
  xaxs = "i",
  ylim = c(-10, 10),
  cex.axis = 0.8
)

points(
  all_data$all_post_data$time.id,
  all_data$all_post_data$RECO_predict_control_mean,
  cex = 0.5,
  col = "red",
  bg = "red",
  pch = 21,
)

abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp_cont$date[366], lwd= 1.5, col = "black")

text(
  x = all_data$all_post_data$time.id[1],
  y = 30,
  paste0("(b)"),
  adj = c(0, 1),
  cex = 0.9
)

mtext(
  side = 2,
  target.plot.var_nee_cont.title[[2]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)


## panel c
par(fig = c(0, 1, 0, 1 / 3), new = TRUE)
plot(
  daily_nee.tmp_cont$date,
  daily_nee.tmp_cont$daily_nee,
  type = "l",
  lwd = 1.5,
  col = "black",
  xlab = "",
  ylab = "",
  xaxt = "n",
  las = 1,
  xaxs = "i",
  ylim = c(-375, 375),
  cex.axis = 0.8
)

lines(daily_nee.tmp_cont$date,
      daily_nee.tmp_cont$daily_gpp,
      lwd = 1.5,
      col = "forestgreen",
      lty = 2
      )

lines(daily_nee.tmp_cont$date,
      daily_nee.tmp_cont$daily_reco,
      lwd = 1.5,
      col = "red",
      lty = 3
      )

abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp_cont$date[366], lwd= 1.5, col = "black")

axis(
  side = 1,
  at = all_data$all_post_data$time.id[month.loc],
  labels = month.ticks,
  tck = -.025,
  cex.axis = 0.8
)

text(
  x = all_data$all_post_data$time.id[1],
  y = 1200,
  paste0("(c)"),
  adj = c(0, 1),
  cex = 0.9
)

mtext(
  side = 2,
  target.plot.var_nee_cont.title[[3]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)

axis(
  side = 1,
  at = c(2019.75, 2020.5, 2021.17),
  label = c(2019, 2020, 2021),
  cex.axis = 0.8,
  tck = -.025,
  lty = 0,
  bty = "n",
  line = 0.9
)

mtext(
  side = 1,
  "Month / Year",
  line = 3,
  outer = FALSE,
  cex = 0.8
)


dev.off()
```


####panel 1 Control half-hourly non-gap filled and half hourly gap filled###
#panel 2 daily GPP_control_mean and Respiration with error lines shaded
#panel 3 cumulative sums of Control NEE, Respiration and neg GPP with error lines

#control side daily data in panel 2
```{r}
 col.code6 <- list(col.name = c( "control (modeled)","control (observed)"),
                               
                  col = c("firebrick1", "firebrick4"))





#figure for cumulative and filled NEE and Reco

target.plot.var_nee_control_daily <- c("NEE_filled_control_mean",
                         "GPP_control_mean",
                         "blank")

target.plot.var_nee_control_daily.title <- c(
  expression(FC ~ '(' ~ mu ~ mol ~ m ^ {-2 } ~ s ^ { -1 } ~ ')'),
  expression(GPP ~ ';' ~ Reco~'(' ~ mu ~ mol ~ m ^ {-2 } ~ d ^ { -1 } ~ ')'),
  expression(Cumulative~sum~ '(' ~ g ~ C ~ m ^ {-2 } ~ ')')
)

## locate the start of each month
month.loc <- which(
  all_data$all_post_data$TIMESTAMP$mday == 1 &
    all_data$all_post_data$TIMESTAMP$hour == 0 &
    all_data$all_post_data$TIMESTAMP$min == 0
)
month.ticks <-
  substr(seq(
    all_data$all_post_data$TIMESTAMP[month.loc[1]],
    all_data$all_post_data$TIMESTAMP[month.loc[length(month.loc)]],
    by = "months"
  ), 6, 7)

## daily average values
daily_nee.tmp_controlside <-
  data.frame(
    date = tapply(
      all_data$all_post_data$time.id,
      all_data$all_post_data$Doy_water,
      min
    ),
    daily_nee = tapply(
      all_data$all_post_data$NEE_filled_control_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp = tapply(
      all_data$all_post_data$GPP_control_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco = tapply(
      all_data$all_post_data$RECO_predict_control_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp_q025 = tapply(
      all_data$all_post_data$GPP_control_q025,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gppq975 = tapply(
      all_data$all_post_data$GPP_control_q975,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco025 = tapply(
      all_data$all_post_data$RECO_predict_control_q025,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco975 = tapply(
      all_data$all_post_data$RECO_predict_control_q975,
      all_data$all_post_data$Doy_water,
      na.mean
    )
  )

#Creating a second daily value data frame that is not cum_sum for panel B
daily_nee.tmp_controlside_2 <-
  data.frame(
    date = tapply(
      all_data$all_post_data$time.id,
      all_data$all_post_data$Doy_water,
      min
    ),
    daily_nee = tapply(
      all_data$all_post_data$NEE_filled_control_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp = tapply(
      all_data$all_post_data$GPP_control_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco = tapply(
      all_data$all_post_data$RECO_predict_control_mean,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gpp_q025 = tapply(
      all_data$all_post_data$GPP_control_q025,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_gppq975 = tapply(
      all_data$all_post_data$GPP_control_q975,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco025 = tapply(
      all_data$all_post_data$RECO_predict_control_q025,
      all_data$all_post_data$Doy_water,
      na.mean
    ),
    daily_reco975 = tapply(
      all_data$all_post_data$RECO_predict_control_q975,
      all_data$all_post_data$Doy_water,
      na.mean
    )
  )

## convert NEE, RECO_predict_control_mean gpp to cumulative sum of carbon
# convert to cumulative carbon
for(ll in 2:8) {
  # convert to daily units
  daily_nee.tmp_controlside[, ll] <-
    daily_nee.tmp_controlside[, ll] * 12 / 1000000 * 1800 * 48
  daily_nee.tmp_controlside[is.na(daily_nee.tmp_controlside[, ll]), ll] <- 0
  
  # calculate cumulative sum, hard-coded with first/second years
  daily_nee.tmp_controlside[1:365, ll] <-
    cumsum(daily_nee.tmp_controlside[1:365, ll])
  daily_nee.tmp_controlside[366:nrow(daily_nee.tmp_controlside), ll] <-
    cumsum(daily_nee.tmp_controlside[366:nrow(daily_nee.tmp_controlside), ll])
  
  # set break (missing value) between two years
  daily_nee.tmp_controlside[366, ll] <- NA
}

## begin plot   
png(
  paste0(out.path, "NEE_control_Daily_growing_post_compost_concord",
    all_data$all_post_data$TIMESTAMP$year[1] + 1900, "_",
    all_data$all_post_data$TIMESTAMP$yday[1] + 1, "_",
    all_data$all_post_data$TIMESTAMP$year[nrow(all_data$all_post_data)] + 1900, "_",
    all_data$all_post_data$TIMESTAMP$yday[nrow(all_data$all_post_data)] + 1, "_",
    "NEE_",
    Sys.Date(), ".png"
  ),
  width = 5.5,
  height = 6,
  units = "in",
  res = 300,
  pointsize = 11,
  bg = "white"
)
par(oma = c(4, 4.5, 0.5, 0.5),
    mar = c(0, 0, 0.25, 0))

par(fig = c(0, 1, 2 / 3, 1), new = FALSE)
plot(
  all_data[[2]]$TIMESTAMP,
  all_data[[2]]$NEE_filled_control_mean,
  pch = 20,
  cex = 0.7,
  col = col.code6$col[1],
  las = 1,
  ylab = "",
  xlab = "",
  xaxt = "n",
  xaxs = "i",
  #yaxt = "n",
  ylim = c(-15, 15),
  cex.axis = 0.8
)

points(
  all_data[[2]]$TIMESTAMP[all_data[[2]]$treatment == "control_post_compost"],
  all_data[[2]]$NEE[all_data[[2]]$treatment == "control_post_compost"],
  pch = 20,
  cex = 0.7,
  col = col.code6$col[2]
)

abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp_controlside$date[366], lwd= 1.5, col = "black")

text(
  x = all_data$all_post_data$time.id[1],
  y = 30,
  paste0("(a)"),
  adj = c(0, 1),
  cex = 0.9,
  
)

mtext(
  side = 2,
  target.plot.var_nee_control_daily.title[[1]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)
legend(
  "topleft",
  fill = col.code6$col,
  border = NA,
  legend = col.code6$col.name,
  ncol = 2,
  cex = 0.7,
  bty = "n"
)

## panel b
par(fig = c(0, 1, 1 / 3, 2 / 3), new = TRUE)
plot(
  daily_nee.tmp_controlside_2$date,
  daily_nee.tmp_controlside_2$daily_gpp,
  xlab = "",
  ylab = "",
  cex = 0,
  col = "forestgreen",
  bg = "forestgreen",
  xaxt = "n",
  las = 1,
  pch = 21,
  xaxs = "i",
  ylim = c(-10, 10),
  cex.axis = 0.8
)
polygon(c(daily_nee.tmp_controlside_2$date,
          rev(daily_nee.tmp_controlside_2$date)),
        c(daily_nee.tmp_controlside_2$daily_reco975 ,
          rev(daily_nee.tmp_controlside_2$daily_reco025)),
        col = col.code5$col3[1],
        border = NA)
polygon(c(daily_nee.tmp_controlside_2$date,
          rev(daily_nee.tmp_controlside_2$date)),
        c(daily_nee.tmp_controlside_2$daily_gppq975 ,
          rev(daily_nee.tmp_controlside_2$daily_gpp_q025)),
        col = col.code5$col3[2],
        border = NA)
points(
  daily_nee.tmp_controlside_2$date,
  daily_nee.tmp_controlside_2$daily_reco,
  cex = 0.6,
  col = "red",
  bg = "red",
  pch = 21,
)

points(
  daily_nee.tmp_controlside_2$date,
  daily_nee.tmp_controlside_2$daily_gpp,
  cex = 0.6,
  col = "forestgreen",
  bg = "forestgreen",
  pch = 21,
)


abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp_controlside$date[366], lwd= 1.5, col = "black")

text(
  x = all_data$all_post_data$time.id[1],
  y = 30,
  paste0("(b)"),
  adj = c(0, 1),
  cex = 0.9
)

mtext(
  side = 2,
  target.plot.var_nee_control_daily.title[[2]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)


## panel c
par(fig = c(0, 1, 0, 1 / 3), new = TRUE)
plot(
  daily_nee.tmp_controlside$date,
  daily_nee.tmp_controlside$daily_nee,
  type = "l",
  lwd = 1.5,
  col = "black",
  xlab = "",
  ylab = "",
  xaxt = "n",
  las = 1,
  xaxs = "i",
  ylim = c(-600, 800),
  cex.axis = 0.8
)

lines(daily_nee.tmp_controlside$date,
      daily_nee.tmp_controlside$daily_gpp,
      lwd = 1.5,
      col = "forestgreen",
      lty = 1
      )

lines(daily_nee.tmp_controlside$date,
      daily_nee.tmp_controlside$daily_reco,
      lwd = 1.5,
      col = "red",
      lty = 1
      )

lines(daily_nee.tmp_controlside$date,
      daily_nee.tmp_controlside$daily_gpp_q025 ,
      lwd = 1,
      col = "forestgreen",
      lty = 3
      )

lines(daily_nee.tmp_controlside$date,
      daily_nee.tmp_controlside$daily_gppq975  ,
      lwd = 1,
      col = "forestgreen",
      lty = 3
      )

lines(daily_nee.tmp_controlside$date,
      daily_nee.tmp_controlside$daily_reco025,
      lwd = 1,
      col = "red",
      lty = 3
      )
lines(daily_nee.tmp_controlside$date,
      daily_nee.tmp_controlside$daily_reco975,
      lwd = 1,
      col = "red",
      lty = 3
      )

abline(
  v = 2020 + 290 / 366,
  col = "red",
  lwd = 2,
  lty = 4
)
abline(h = 0, col = "black")
abline(v = daily_nee.tmp_controlside$date[366], lwd= 1.5, col = "black")

axis(
  side = 1,
  at = all_data$all_post_data$time.id[month.loc],
  labels = month.ticks,
  tck = -.025,
  cex.axis = 0.8
)

text(
  x = all_data$all_post_data$time.id[1],
  y = 1200,
  paste0("(c)"),
  adj = c(0, 1),
  cex = 0.9
)

mtext(
  side = 2,
  target.plot.var_nee_control_daily.title[[3]],
  line = 3,
  outer = FALSE,
  cex = 0.8
)

axis(
  side = 1,
  at = c(2019.75, 2020.92, 2021.17),
  label = c(2019, 2020, 2021),
  cex.axis = 0.8,
  tck = -.025,
  lty = 0,
  bty = "n",
  line = 0.9
)

mtext(
  side = 1,
  "Month / Year",
  line = 3,
  outer = FALSE,
  cex = 0.8
)


dev.off()
```

#Cumlative sum Reco control side pre compost application with CI's

```{r}
cumsum(tapply(all_data$all_pre_data$RECO_predict_control_mean,
            round(all_data$all_pre_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48


cumsum(tapply(all_data$all_pre_data$RECO_predict_control_q025,
            round(all_data$all_pre_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48


cumsum(tapply(all_data$all_pre_data$RECO_predict_control_q975 ,
            round(all_data$all_pre_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48

```
#Cumlative sum GPP control side pre compost application with CI's

```{r}
cumsum(tapply(all_data$all_pre_data$GPP_control_mean ,
            round(all_data$all_pre_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48


cumsum(tapply(all_data$all_pre_data$GPP_control_q025,
            round(all_data$all_pre_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48


cumsum(tapply(all_data$all_pre_data$GPP_control_q975 ,
            round(all_data$all_pre_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48
```


#Cumlative sum Reco control side post compost application with CI's
```{r}

#Reco Cumsum
cumsum(tapply(all_data$all_post_data$RECO_predict_control_mean,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48

#lower bound CI
cumsum(tapply(all_data$all_post_data$RECO_predict_control_q025,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48

#upper bound CI
cumsum(tapply(all_data$all_post_data$RECO_predict_control_q975 ,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48
```
#Cumulative sum GPP control side post compost application with CI's

```{r}

#GPP Cumsum
cumsum(tapply(all_data$all_post_data$GPP_control_mean,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48

#comparison of GPP cum sum using reco predict

#making the comparison variable
all_data$all_post_data$GPP_control_with_pred_Reco <- (all_data$all_post_data$NEE_filled_control_mean - all_data$all_post_data$RECO_predict_control_mean)


#cumsum the comparison variable
cumsum(tapply(all_data$all_post_data$GPP_control_with_pred_Reco,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48
######################################################################
#lower bound CI
cumsum(tapply(all_data$all_post_data$GPP_control_q025,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48


######################################################################


#upper bound CI
cumsum(tapply(all_data$all_post_data$GPP_control_q975 ,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48
```




#Cumlative sum Reco Treatment side pre compost application with CI's

```{r}
cumsum(tapply(all_data$all_pre_data$RECO_predict_treatment_mean,
            round(all_data$all_pre_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48



cumsum(tapply(all_data$all_pre_data$RECO_predict_treatment_q025,
            round(all_data$all_pre_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48


cumsum(tapply(all_data$all_pre_data$RECO_predict_treatment_q975 ,
            round(all_data$all_pre_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48

```

#Cumlative sum GPP treatment side pre compost application with CI's

```{r}
cumsum(tapply(all_data$all_pre_data$GPP_treatment_mean ,
            round(all_data$all_pre_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48


cumsum(tapply(all_data$all_pre_data$GPP_treatment_q025,
            round(all_data$all_pre_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48


cumsum(tapply(all_data$all_pre_data$GPP_treatment_q975 ,
            round(all_data$all_pre_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48
```


#Cumlative sum Reco Treatment side post compost application with CI's
```{r}

#Reco Cumsum
cumsum(tapply(all_data$all_post_data$RECO_predict_treatment_mean,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48



#lower bound CI
cumsum(tapply(all_data$all_post_data$RECO_predict_treatment_q025,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48

#upper bound CI
cumsum(tapply(all_data$all_post_data$RECO_predict_treatment_q975 ,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48
```
#Cumulative sum GPP treament side post compost application with CI's

```{r}

#GPP Cumsum
cumsum(tapply(all_data$all_post_data$GPP_treatment_mean,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48


########GPP comparison using Reco_predict######

#making the comparison GPP variable
all_data$all_post_data$GPP_treatment_with_pred_Reco <- (all_data$all_post_data$NEE_filled_treatment_mean - all_data$all_post_data$RECO_predict_treatment_mean)

all_data$all_post_data$GPP_treatment_with_pred_Reco [(all_data$all_post_data$Rg <= 10 )]<-0


cumsum(tapply(all_data$all_post_data$GPP_treatment_with_pred_Reco,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48

#essentially the same. -387.73 with this v.s -387.66

#lower bound CI
cumsum(tapply(all_data$all_post_data$GPP_treatment_q025,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48

#upper bound CI
cumsum(tapply(all_data$all_post_data$GPP_treatment_q975 ,
            round(all_data$all_post_data$Doy_water ),
            function(x) mean(x,na.rm=T)))*12/1000000*1800*48
```


```{r}
all_data$all_pre_data
write.csv(
  all_data$all_pre_data,
  paste0(out.path, Sys.Date(), "_all_pre_compost_data.csv"),
  quote = T,
  row.names = F
)

all_data$all_post_data
write.csv(
  all_data$all_pre_data,
  paste0(out.path, Sys.Date(), "_all_post_compost_data.csv"),
  quote = T,
  row.names = F
)
```


